Abstract: Blockchain based IoT data management can include receiving IoT data are with one or more processing units. At least one aggregation pattern of the IoT data can be determined by one or more processing units. The IoT data can be hashed, based upon the at least one aggregation pattern to obtain hash values of the IoT data by one or more processing units. The hash values can be sent to a blockchain system for storing by one or more processing units.

Abstract: There is provided a method of operating a certifying node to certify incremental trained machine learning (ML) models of one or more edge devices associated with a peer to peer network. The method includes (i) generating a predictive outcome value for a test data set by executing a candidate ML model against the test data set available to the certifying node; (ii) determine a measure of quality of the candidate ML model by matching the predictive outcome value of the candidate ML model with an actual outcome value of the test data set; and (iii) certify the candidate ML model by comparing the measure of quality of the candidate ML model against a threshold error value, for use in real time incremental training by the one or more edge devices of the peer to peer network.

Abstract: A method for optimizing network decision nodes is described. In one embodiment, the method includes generating an initial network of decision nodes constructed according to one or more rules, rearranging one or more decision nodes of the initial network based at least in part on a conversion of the one or more rules to a disjunctive normal form, analyzing the rearranged network of decision nodes in an upstream direction, and optimizing the rearranged network by merging two or more decision nodes of the rearranged network based at least in part on the analysis of the rearranged network. In some cases, the decision nodes include one or more levels of parent nodes and child nodes, each level of child nodes being connected to respective parent nodes. In some cases, the upstream direction is in a direction from the child nodes to the parent nodes.

Abstract: Embodiments of the present invention provide a computer-implemented method for performing unsupervised time-series feature learning. The method generates a set of reference time-series of random lengths, in which each length is uniformly sampled from a predetermined minimum length to a predetermined maximum length, and in which values of each reference time-series in the set are drawn from a distribution. The method generates a feature matrix for raw time-series data based on a set of computed distances between the generated set of reference time-series and the raw time-series data. The method provides the feature matrix as an input to one or more machine learning models.

Abstract: An advanced intelligence engine (AIE) for use in identifying what may be complex events or developments on one or more data platforms or networks from various types of structured or normalized data generated by one or more disparate data sources. The AIE may conduct one or more types of quantitative, correlative, behavioral and corroborative analyses to detect events from what may otherwise be considered unimportant or non-relevant information spanning one or more time periods. Events generated by the AIE may be passed to an event manager to determine whether further action is required such as reporting, remediation, and the like.

Abstract: Embodiments of the present invention provide a system and methods to mitigate poisoning attacks within machine learning systems. The invention includes an improved data analysis approach to train an ensemble of machine learning models to analyze received data and label the data in a non-binary fashion to indicate likelihood that certain data has been injected abnormally and should not be used for training purposes. The resulting dataset from the ensemble is assessed to determine convergence of model labeling and to detect outlier data labeling among models in the ensemble. Confidence scores for clustered interaction data may be performed on varied sets of training data populations and using a number of models. Output from the various training/model mixes are fed to a machine learning model to compare ensemble accuracy between different model sets and select the most accurate ensemble combination.

Abstract: Aspects of the subject disclosure may include, for example, a device having a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of deriving one or more building blocks from original content of a franchise; providing a search interface enabling a user to discover the one or more building blocks, wherein the user selects a set of building blocks from the one or more building blocks for creating user generated content; providing a composition module enabling the user to manipulate the set of building blocks to create the user generated content; and applying one or more policies of the franchise concerning the user generated content, wherein the one or more policies ensure that the user generated content complies with objectives of the franchise. Other embodiments are disclosed.

Abstract: Use of a deduplication module remote from a storage system for storage of data subject to deduplication by the remote deduplication module. The storage system may generate a hash of target data and send the hash of the target data to the remote deduplication module. The remote deduplication module may access a hash database corresponding to the storage system and apply a deduplication algorithm on the hash. In turn, information regarding a deduplication result may be returned to the storage system which may either store the unique target data or update a mapping between the target data and duplicate existing data in a persistent storage of the storage device. The remote deduplication module may be executed by scalable cloud resources such that the computational resources dedicated to the deduplication module may be scaled.

Abstract: A system and method for detecting elevator maintenance behaviors in an elevator hoistway. The system of the invention comprises: an emitting component for emitting wireless signals including channel state information (CSI) into the elevator hoistway; a receiving component for receiving the CSI from the elevator hoistway; and a maintenance behavior detecting device, which comprises: a memory that stores CIS image recognition models constructed corresponding to the predefined elevator maintenance behaviors and a corresponding computer program executable on a processor, and the processor configured to be able to execute the computer program to implement the following operations: processing data of the received CSI to obtain a CSI image corresponding to the elevator maintenance behavior detected, and inputting the CSI image to the CSI image recognition model for analysis and processing so as to detect the elevator maintenance behavior.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for data aggregation.

Abstract: Devices and techniques are generally described for fraud detection. A machine learning model is used to determine a first fraud risk score for a first transaction. The machine learning model includes a first set of weights. A first covariance matrix is determined for the machine learning model based at least in part on the first fraud risk score. A second set of weights for the machine learning model is determined. The second set of weights is determined based on the first set of weights and the first covariance matrix. In various examples, the machine learning model with the second set of weights is used to determine a second fraud risk score for a second transaction. A fraud decision surface is determined and the second fraud risk score is compared to the fraud decision surface. Data indicating that the second transaction is fraudulent is sent to a computing device.

Abstract: Computing systems, for example, multi-tenant systems create data centers in a cloud platform using a cloud platform infrastructure language that is cloud platform independent. The system receives a cloud platform independent declarative specification describing a new datacenter to be created and a customization specification for customizing the data center. The system compiles the declarative specification along with the customization specification to generate a metadata representation of the data center. The metadata representation is used to generate a data center on a target cloud platform. Different customization specifications can be provided to generate different customized datacenters based on the same declarative specification. For example, the different customized data centers may implement different policies, for example, network policies, security policies, and so on.

Abstract: One embodiment provides a system for facilitating anomaly detection. During operation, the system determines, by a computing device, a set of training instances, wherein a training instance represents a single class of data within a predefined range. The system computes a similarity score for each testing instance in a set of testing instances, wherein the similarity score is based on a similarity function which takes as input a respective testing instance and the set of training instances. The system determines a boundary threshold based on an ordering of the similarity score for each testing instance. The system classifies a first testing instance as an anomaly responsive to determining that the first testing instance falls outside the boundary threshold, thereby enhancing data mining and outlier detection in the single class of data using unlabeled training instances.

Abstract: A device that includes an enterprise data indexing engine (EDIE) configured to receive a set of sentences and to compare the words in the sentences to a set of predefined keywords. The EDIE is further configured to identify one or more sentences that do not contain any of the keywords and to associate the identified sentences with a first classification type. The EDIE is further configured to identify a sentence that contains one or more keywords and to associate the sentence with a second classification type. The EDIE is further configured to link together the sentence that is associated with the second classification type and the sentences that are associated with the first classification type.

Abstract: A non-transitory computer-readable recording medium storing a program that causes a computer to execute a process for evaluating a voice, the process includes analyzing a voice signal to detect a pitch frequency; selecting an evaluation target region to be evaluated in the detected pitch frequency based on a distribution of a detection rate of the detected pitch frequency; and evaluating a voice based on the distribution of the detection rate and the selected evaluation target region. An Impression of the voice using a correction to the distribution is evaluated and determined as good when a spread of the corrected distribution is larger than or equal to a certain threshold.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for rule generation and interaction. A rules engine is provided that does not require manual effort to generate or maintain high-quality rules over time for detecting malicious accounts or events. Rules no longer need to be manually added, tuned, or removed from the system. The system is able to determine the health of each rule and automatically add, tune, and remove rules to maintain a consistent, effective rule set.

Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that, upon identifying a set of distribution contacts, generate clusters of distribution contacts from a sampled subset of distribution contacts and assign remaining distribution contacts from the set to the generated clusters for visualization in a user interface. By clustering a representative sample of such distribution contacts, the disclosed methods, non-transitory computer readable media, and systems can quickly analyze and identify contact characteristics in clusters of distribution contacts, including common contact characteristics exhibited by a given cluster's contacts. The disclosed methods, non-transitory computer readable media, and systems can accordingly respond to user requests for a cluster analysis by expeditiously generating cluster visualizations identifying contact characteristics of clustered distribution contacts.

Abstract: Media items associated with status values are stored using a server in a protected environment. A device outside the protected environment requests sending media items to a client device that is also outside. Using machine learning systems that can be trained using attribute values associated with personal data records to output the existence of a status value, the server computer trains a particular machine learning system in the protected environment only if specified data meets specified criteria, then sends the resulting trained ML system to the requesting device. That device evaluates the trained ML system to determine which media items to deliver to the client device in the manner set forth in the claims.

Abstract: Within a portion of a currently executing compiled streams application according to a request to modify a configuration of the portion, a placement is performed of an operator of the currently executing compiled streams application into a processing element, wherein the currently executing compiled streams application has a configuration assigned before application execution began. In response to the placement, a resource allocation corresponding to the placement is performed according to the request, the resource allocation specifying a resource assigned to the processing element. The processing element is activated to execute the operator according to the placement and the resource allocation.

Abstract: An approach is provided that detects transmission of sensitive data fields from a user device to an online site. The approach determines the data types of the sensitive data fields and gathers site information from the online site which are stored in a data store along with the current date. Subsequently, the user can manage the data by displaying site information on a display of the user device. Each record of site information pertains to one of many online sites including the selected online site. The user makes a data management request at the user device pertaining to the selected site information. The approach then responsively transmits a request to the selected online site with the request being based on the received data management request. One example of a request is a deletion request that requests that the online site remove the user's sensitive data from the online site.

Abstract: A method, system and computer program product for generating an optimization model. A prescriptive model including a structured set of data and instance(s) of templates of a technical and/or business prescriptive domain is received, where the instances of the templates define goals and/or constraints for the structured set of data. The business prescriptive domain extends the technical prescriptive domain defining a generic optimization problem and the business prescriptive domain defines a more specific business problem. The prescriptive model is translated into a technical prescriptive model using a set of mapping rules to translate the instance(s) of the templates of the business prescriptive domain into instance(s) of the templates of the technical prescriptive domain from which the business prescriptive domain extends. Furthermore, the technical prescriptive model is translated into an optimization model, where the optimization model is solved.

Abstract: Techniques to correlate event data are disclosed. In various embodiments, an aggregation engine is used to correlate event data received from one or more source systems based on one or more correlation rules. An event group comprising at least a portion of said correlated event data is generated programmatically and is asserted as a fact in a working memory of a Rete engine configured to apply one or more Rete rules to facts in the working memory.

Abstract: An image classification system includes a storage device, a computing device and a first processing device. The storage device stores a plurality of pseudo-centroid datasets, wherein the pseudo-centroid datasets correspond to a plurality of units of first image dataset, and the number of pseudo-centroid data points of each of the pseudo-centroid datasets is much smaller than the number of data points of each of the units of first image dataset. The computing device receives the second image data and computes a plurality of feature values of the second image data. The first processing device receives the feature values and the pseudo-centroid datasets, and compares the feature values with the pseudo-centroid data points to identify and classify the second image data.

Abstract: The present disclosure provides an information extraction method. The method includes obtaining an unstructured text data; parsing the unstructured text data based on a node format; and generating a first node set that comprises nodes describing the unstructured text data. The method further includes obtaining a rule database, the rule database comprising a plurality of rules used for generating a node, each rule designating a role of a node generated by using the rule, and the role of the node being a root node or a non-root node; synthesizing the nodes in the first node set based on the rule database to generate a root node; and generating structured data based on the root node.

Abstract: Methods, systems, and computer program products are provided. A network device receives, from a client device, a description of a dataset to be de-identified, and a list of one or more data de-identification techniques selected from groups consisting of a group of data masking techniques and a group of data pseudonymization techniques, and their configuration options supported by the client device. A first technique, from the at least one group of techniques and its configuration options supported by the client device and the network device are determined. The network device receives a dataset produced at the client device by applying the first technique and selected configuration options to corresponding attributes from the client device. The network device applies a de-identification technique to the dataset to produce a resulting set of de-identified data, wherein the de-identification technique is coordinated with the first technique and its configuration options to de-identify the dataset.

Abstract: Embodiments for localizing abnormal energy consumption at a facility in a cloud computing environment by a processor. One or more residuals for both one or more predictors and a prediction may be generated according to one or more energy consumption measurements, weather data, and one or more characteristics of the one or more facilities, or a combination thereof. An energy consumption anomaly may be localized according to those of the one or more residuals associated with one or more predictors having an actual energy measurement deviating from a predicted actual energy measurement.

Abstract: The present invention relates to a method for composing an application using a plurality of distributed interaction patterns including services, events, actions and properties. A second semantic model or Thing Recipe is provided as a basis for representing the application. This Thing Recipe provides a concept for orchestration or choreography of interaction patterns distributed across a network. The process from a specification of the Thing Recipe to its deployment is considerable shorter than the implementation and orchestration of web service. The Thing Recipe is not only interpretable but also executable. The semantic approach of the Thing Recipe enables a formal representation capable of a machine-based discovery of the application functionality and, at the same time, a machine interpretation and execution. This means that a Thing Recipe can be directly executed in any device that has an embedded runtime with reasoning capabilities.

Abstract: Certain embodiments involve determining and outputting correlations between metrics in large-scale web analytics datasets. For example, a processor identifies pairs of data metrics in a web analytics data set and determines a Maximal Information Coefficient (MIC) score for each pair of data metrics that indicates a strength of a correlation between the pair of data metrics. The processor generates an interactive user interface that graphically displays each pair of correlated data metrics having an MIC score above a threshold and the interactive user interface indicates the strength of the correlation between each displayed pair of correlated data metrics. The processor receives user input indicating an adjustment to the threshold and modifies the interactive user interface in response to receiving the user input by adding pairs of correlated data metrics to, or removing pairs of correlated metrics from, the interactive user interface based on the adjustment to the threshold.

Abstract: Patient treatment may be optimized using Recurrent Neural Network (RNN) based state simulation and Reinforcement learning (RL) techniques to simulate future states and actions. A RNN state simulator and a RL action generator may be trained using patient data such as historical states and actions. The RL action generator may be optimized by applying the RNN state simulator to simulating future states and applying the RL action generator to generate recommended actions based on the simulated future states. This process may be iteratively performed until a computational convergence is reached by the RL action generator which may indicate that the RL action generator has been optimized. A patient state may be fed into the optimized RL action generator to generate an optimal recommended treatment action.

Abstract: An example method for assisting a patient to conduct a physical therapy session can include: presenting information about the physical therapy session on a display, the information including one or more actions to be performed by the patient; capturing one or more images of the patient as the patient performs the one or more actions; detecting, by a computing device, a safety risk related to one or more medical devices associated with the patient during the physical therapy session; and providing feedback regarding the safety risk. Other methods can include: detecting, by a computing device, using the one or more images, a level of effort from the patient during the physical therapy session; and modifying a future physical therapy session for the patient based upon the level of effort; or allowing the patient to compete with other patients during the physical therapy session based upon the level of effort.

Abstract: In one embodiment, a method includes accessing policy states associated with a policy, determining that a particular policy state of the policy states should not be pruned, and generating possible next policy states for the particular policy state based on one or more policy actions and one or more costs associated with the respective policy actions, wherein each policy action is associated with one or more probabilities corresponding to one or more possibilities of resulting in one or more of the possible next policy states, wherein the respective cost associated with the policy action is determined based on one or more computing costs of the policy action resulting in the one or more of the possible next policy states in combination with the respective one or more probabilities, and wherein each policy state of the created policy evaluation tree comprises a rule state for each rule of the policy.

Abstract: A method for improving memory efficiency of production rule systems is described. In one embodiment, the method includes identifying a rule associated with production rule systems, constructing a production rule network based at least in part on the rule, identifying a positional constraint associated with the rule, and implementing an alpha memory gate in the production rule network based at least in part on the positional constraint. In some cases, the alpha memory gate is one of a plurality of nodes of the production rule network.

Abstract: An example integrated circuit includes a memory including a non-deterministic finite automata (NFA) buffer configured to store a plurality of instructions defining an ordered sequence of instructions of at least a portion of an NFA graph, the portion of the NFA graph comprising a plurality of nodes arranged along a plurality of paths. The NFA engine determines a current symbol and one or more subsequent symbols of a payload segment that satisfy a match condition specified by a subset of instructions of the plurality of instructions for a path of the plurality of paths and in response to determining the current symbol and the one or more subsequent symbols of the payload segment that satisfy the match condition, outputs an indication that the payload data has resulted in a match.

Abstract: The present invention relates to a method and system for determining the quality of an application based on user behaviors of application management. The method includes: 1) preprocessing data on user behaviors of application management to obtain a sequence of management behaviors as a training set; 2) extracting features of the user behaviors from the obtained sequence of management behaviors; 3) establishing a multi-dimensional independent variable space using the extracted behavior features, wherein each application in the training set is considered as a point in the space; training a model according to the space; and debugging to obtain optimal model parameters and a definite regression model; 4) arranging the data of the management behaviors of each application to be predicted according to step 1) and step 2), and then inputting the result into the regression model, wherein an output of the regression model is the result of the application quality determination.

Abstract: Exemplary methods, apparatuses, and systems receive, from a first user device associated with a first user, location data representing a current physical location of the first user. A request for the current physical location of the first user is received from a second user device associated with the second user. In response to the received selection, the current physical location of the first user based upon the shared location data is transmitted to the second user device. A selection of a third party service is received from the second user device. The current physical location of the first user is transmitted to a server associated with the third party service within a request for the third party service to send the a service or product to the current physical location of the first user.

Abstract: Methods and systems for a quantum computing approach to solving challenging, e.g., NP-complete, problems in transportation. One of the methods includes (a) ingesting transportation-related data into a graph structure, the transportation-related data being associated with a transportation system; (b) identifying a transportation metric associated with the transportation system; (c) identifying at least one attribute associated with the transportation-related data, where the transportation metric is based at least in part on the attribute; (d) using a quantum computer to derive an operational parameter for the attribute that improves the transportation metric; and (e) applying the operational parameter to the operation of the transportation system.

Abstract: A method of unlocking an electronic device, an unlocking device and system and a storage medium are provided. The method includes: acquiring at least one image to be authenticated, in which the image to be authenticated is an RGB image or an infrared image; obtaining an RGB image authentication result of the image to be authenticated by RGB object authentication of the image to be authenticated in a case where the image to be authenticated is the RGB image, and obtaining an infrared image authentication result of the image to be authenticated by infrared object authentication of the image to be authenticated in a case where the image to be authenticated is the infrared image; and determining whether to unlock the electronic device according to at least one of the RGB image authentication result and the infrared image authentication result.

Abstract: The disclosure provides an approach for learning to schedule control fragments for physics-based virtual character simulations and physical robot control. Given precomputed tracking controllers, a simulation application segments the controllers into control fragments and learns a scheduler that selects control fragments at runtime to accomplish a task. In one embodiment, each scheduler may be modeled with a Q-network that maps a high-level representation of the state of the simulation to a control fragment for execution. In such a case, the deep Q-learning algorithm applied to learn the Q-network schedulers may be adapted to use a reward function that prefers the original controller sequence and an exploration strategy that gives more chance to in-sequence control fragments than to out-of-sequence control fragments.

Abstract: Systems, devices, media and methods are presented for splitting decision trees between server and client. The client of the systems and methods sends a configuration query. The server of the system and method receives the configuration query. The server retrieves Config rule(s) according to the configuration query. Each of the Config rule(s) can be represented by decision tree(s). The server evaluates the decision tree(s). If a definitive True or False cannot be derived from the evaluation using server knowledge, the server prunes the decision tree(s) and returns them to client side for further evaluation.

Abstract: Embodiments for detection of energy consumption anomalies in one or more energy consumption systems in a cloud computing environment by a processor. Energy consumption may be predicted for one or more facilities according to one or more energy consumption measurements, weather data, and one or more characteristics of the one or more facilities, or a combination thereof. An onset of an energy consumption anomaly may be detected according to the prediction.

Abstract: Example embodiments relate to parallelizing structured query language (SQL) user defined transformation functions. In example embodiments, a subquery of a query is received from a query engine, where each of the subqueries is associated with a distinct magic number in a magic table. A user defined transformation function that includes local, role-based functionality may then be executed, where the magic number triggers parallel execution of the user defined transformation function. At this stage, the results of the user defined transformation function are sent to the query engine, where the query engine unions the results with other results that are obtained from the other database nodes.

Abstract: Techniques are disclosed for standardization of data. According to a first technique, standard representation terms are determined for to-be-standardized data using the to-be-standardized data itself and without using any external reference data. According to a second technique, a combination of the to-be-standardized data and an external reference is used to determine standard representation terms for the to-be-standardized data.

Abstract: Patient treatment may be optimized using Recurrent Neural Network (RNN) based state simulation and Reinforcement learning (RL) techniques to simulate future states and actions. A RNN state simulator and a RL action generator may be trained using patient data such as historical states and actions. The RL action generator may be optimized by applying the RNN state simulator to simulating future states and applying the RL action generator to generate recommended actions based on the simulated future states. This process may be iteratively performed until a computational convergence is reached by the RL action generator which may indicate that the RL action generator has been optimized. A patient state may be fed into the optimized RL action generator to generate an optimal recommended treatment action.

Abstract: An illicit website identifying (IWI) computing device is described herein, the IWI computing device configured to store a log of suspect illicit websites, the log including website content associated with each suspect illicit website. Each suspect illicit website is associated with a respective merchant. The IWI computing device is also configured to receive transaction data for at least one transaction initiated at a first suspect illicit website, the first suspect illicit website included within the log of suspect illicit websites, and analyze the received transaction data to identify one or more transactions including at least a portion of the website content associated with the first suspect illicit website. The IWI computing device is further configured to identify the first suspect illicit website and the merchant associated therewith as illicit for further investigation, and generate an identification report including the identified illicit website and merchant.

Abstract: Aspects of the disclosed technology provide a method including receiving one or more signals emanated from a monitored device; signal processing, based on a software model and a hardware-software (HW/SW) interaction model of the monitored device, the one or more signals to determine if an anomaly exists in the one or more signals; and responsive to determining, based on the signal processing, that an anomaly exists, transmitting an indication of the anomaly.

Abstract: Provided is an. emotion. estimating apparatus capable of precisely estimating an emotion and mental state of a measurement subject by using a non-contact pulse detection technology. The emotion estimating apparatus forcibly performs re-sampling processing of digital biometric data generated by converting a heart rate signal to digital data after extracting data for one cycle at an RR interval, and obtains coefficients of harmonic components by a DCT conversion processing unit. From the coefficients of the harmonic components, AC components are removed by LPFs, whereby a coefficient data array is obtained.. The coefficient data array is compared to a dictionary data group, in which dictionary data is a characteristic quantity indicating an emotion or mental state, and a similarity therebetween is calculated, whereby an emotion or mental state of a subject is estimated on the basis of the heart rate signal of the subject.

Abstract: In accordance with embodiments, there are provided mechanisms and methods for managing access to data based on information associated with a physical location of a user. These mechanisms and methods for managing access to systems, products, or data based on information associated with a physical location of a user can enable improved data management efficiency, enhanced data management accuracy, decreased data management costs, decreased licensing costs, increased security, additional marketing opportunities, etc.

Abstract: According to examples, an apparatus may include a machine readable medium on which is stored machine readable instructions that may cause a processor to, for each of a plurality of resource setting levels, determine resource usage characteristics and execution times of executed workloads, assign, based on the resource usage characteristics of the executed workloads, each of the executed workloads into one of a plurality of resource bins, determine, for each of the resource bins, an average execution time of the executed workloads in the resource bin, determine a total average execution time of the executed workloads from the determined average execution times, identify a lowest total average execution time of the determined total average execution times, determine the resource setting level corresponding to the identified lowest total average execution time, and tune a resource setting to the determined resource setting level.

Abstract: The invention relates to a method for triggering one or several actions complementary to those triggered by an application method implemented by an electronic device. The invention further relates to a system including one or several electronic devices implementing said method triggering complementary actions to cooperate with one or several communicating electronic peripherals.

Abstract: Methods, systems, and computer-readable storage media for ranking anomaly detection algorithms, including operations of receiving a set of unlabeled data from one or more sensors in a plurality of sensors of an internet of things, generating a plurality of data distributions corresponding to the set of unlabeled data by using a plurality of anomaly detection algorithms, and ranking the plurality of anomaly detection algorithms relative to the set of unlabeled data based on a distance between a first quantile and a second quantile of each of the plurality of data distributions.