Abstract: A multiple presentation fidelity-level based quantitative cyber risk decision support system. The system comprises a processor, a memory, a data store comprising cyber threat event log entries, and a cyber threat analysis application stored in the memory that, when executed by the processor, analyzes the log entries, executes cyber threat materialization probability update rules on risk elements based on analyzing the log entries, builds a first presentation of an amplitude of cyber risk using a first presentation method, presents the first presentation on a workstation associated with a first level of user, builds a second presentation comprising an ordered list of actions to perform to mitigate cyber risk using a second presentation method, and presents the second presentation on a workstation associated with a second level of user.

Abstract: A system and a computer-implemented method are provided for segmenting an object in a medical image using a graphical segmentation interface. The graphical segmentation interface may comprise a set of segmentation tools for enabling a user to obtain a first segmentation of the object in the image. This first segmentation may be represented by segmentation data. Interaction data may be obtained which is indicative of a set of user interactions of the user with the graphical segmentation interface by which the first segmentation of the object was obtained. The system may comprise a processor configured for analyzing the segmentation data and the interaction data to determine an optimized set of user interactions which, when carried out by the user, obtains a second segmentation similar to the first segmentation, yet in a quicker and more convenient manner. A video may be generated for training the user by indicating the optimized set of user interactions to the user.

Abstract: Smart electricity monitors with unique identities placed at individual power outlets within a building communicate frequent power measurements to a service which determines, from this power usage data, which outlets are associated with occupied seats within the building. This occupancy information can be used to update an occupancy model for the building that is used to forecast the building's occupancy. Based on present occupancy, projected occupancy, and other data, in some instances, the building's thermostats can be controlled and unoccupied seats can be assigned dynamically.

Abstract: Disclosed is a system for removing bugs present in a software code. A determination module determines a usage pattern of a software code by using an Artificial Neural Network (ANN) technique. A comparison module compares the usage pattern with a set of pre-stored usage patterns of software applications similar to the software code. An execution module executes a set of test suites, on the software code, associated to at least one software application of the software applications, when a usage pattern of the at least one software application is matched with the usage pattern of the software code. An identification module identifies a code snippet comprising the bug. A recommendation module recommends a code patch, corresponding to the code snippet, from a ranked list of code patches determined by a Deep RNN technique. Further, a replacement module replaces the code snippet with the code patch thereby removing the bug.

Abstract: Systems and methods for generating synthetic data are disclosed. For example, a system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving a dataset that includes time series data having a plurality of dimensions and generating a transformed dataset by performing a first data transformation. The first data transformation may include a time-based data processing method. The operations may include generating a synthetic transformed-dataset by implementing a data model using the transformed dataset. The data model may be configured to generate synthetic transformed-data based on a relationship between data of at least two dimensions of the transformed dataset. The operations may include generating a synthetic dataset by performing a second data transformation on the synthetic transformed-dataset. The second data transformation may include an inverse of the first data transformation.

Abstract: In one embodiment, an agent process produces synthetic packet traffic and iteratively performs a sub-process that determines isolated network segments of the communication channel between intermediate nodes and computes a set of network metrics for the isolated network segments based at least in part on incrementing TTL expiry error data points. The sub-process also encapsulates, for inclusion within the next packet to be sent, a list of intermediate node IDs along the communication channel up to a latest received node ID and computed sets of network metrics for respective network segments. The agent process may then generate, upon termination of the sub-process, a report, the report including the list of intermediate node IDs along the communication channel up to a latest received node ID and computed sets of network metrics for respective network segments.

Abstract: Predicting a malfunction of a component of a unit includes providing a transition matrix of a parameter of the component, wherein the transition matrix includes for a number of discrete value states of the parameter probabilities to switch from one discrete value state to another within a certain time period; providing the conditional probability distribution for the malfunction given the discrete value states; providing a current discrete value state of the parameter; determining a conditional probability distribution of the discrete value states given the current discrete value state for a future point in time based on the current discrete value state and on the transition matrix by use of a Markov chain; and determining a probability for the malfunction for the future point in time based on the conditional probability distribution of the discrete value states for the future point in time and the conditional probability distribution for the malfunction.

Abstract: Novel distractorless authorship verification technology optionally combines with novel algorithms to solve authorship attribution as to an open set of candidates—such as without limitation by analyzing the voting of “mixture of experts” and outputting the result to a user using the following: if z (z=pi?pj?pi+pj?(pi?pj)2/n) is larger than a first predetermined threshold then author j cannot be the correct author; or if z (z=pi?pj?pi+pj?(pi?pj)2/n) is smaller than a second predetermined threshold then author i cannot be the correct author; or if no author garners significantly more votes than all other contenders then none of the named authors is the author of a document in question—in a number of novel applications. Personality profiling and authorship attribution may also be used to verify user identity to a computer.

Abstract: Aspects of the subject disclosure may include, for example, receiving a plurality of media items, each including an object having a plurality of variables, performing adaptive analysis and transfer analysis on the media items, generating a model based on both the adaptive analysis and the transfer analysis and recognizing, using the model, the object in media content. The model may also provide a description of at least one of the variables of the object in the media content, and/or how that variable relates to the object. The variables may be known in the media items and unknown in the media content. The adaptive analysis and/or the transfer analysis may be performed iteratively and/or simultaneously. The variables may include an intensity of the object, a viewpoint from which the object is observed, an illumination of the object, a blur of the object, and/or other variables. Other embodiments are disclosed.

Abstract: Systems and methods for retrieval of information from read-only databases that hold taxonomic-related and sequence-related data. A method may include receiving organism names from a taxonomy database and detecting new organism names. The method may also include retrieving hierarchical data and assigning the new organism names to buckets based on the hierarchical data. The method may further include receiving sequence data elements from a nucleotide database, identifying particular buckets to correspond to a screener data set, querying organism names assigned to the particular buckets with names of reference sequences of the sequence data elements, generating a mapping between the sequence data elements and organism names returned as a result of the queries, and storing the mapping.

Abstract: Various embodiments of the present technology generally relate to systems and methods for self-healing services and automatic recovery of distribute systems. Some embodiments of the present technology leverage all the available synthetic, customer, client, server, support signals from various sources to intelligently and in real-time detect outages, root cause outages to recoverable targets (e.g., for auto recovery actions), identify the right engineering teams (e.g., for faster manual mitigation), and perform the appropriate recovery action (such as recycle service, reboot server, switch out a faulty rack) or other mitigation actions such as routing, collecting debug information, alerting to the right team, or alert suppression. Some embodiments separate signal monitoring and workflow coordination.

Abstract: A system, method and computer program product for disambiguating one or more entity mentions in one or more documents. The method facilitates the simultaneous linking entity mentions in a document based on convolution neural networks and recurrent neural networks that model both the local and global features for entity linking. The framework uses the capacity of convolution neural networks to induce the underlying representations for local contexts and the advantage of recurrent neural networks to adaptively compress variable length sequences of predictions for global constraints. The RNN functions to accumulate information about the previous entity mentions and/or target entities, and provide them as the global constraints for the linking process of a current entity mention.

Abstract: The present disclosure provides a hybrid vehicle and a method of predicting a driving pattern in the same. The method includes: acquiring current vehicle driving information, determining an upcoming event and a driving style based on the current vehicle driving information, and generating an acceleration/deceleration prediction value based on a prediction model corresponding to the upcoming event and the driving style selected from a plurality of pre-learned prediction models.

Abstract: Methods and systems are provided for reducing noise in medical images with deep neural networks. In one embodiment, a method for training a neural network comprises transforming each of a plurality of initial image data sets not acquired by a medical imaging modality into a target image data set, wherein each target image data set is in a format specific to the medical imaging modality, corrupting each target image data set to generate a corrupted image data set, and training the neural network to map each corrupted image data set to the corresponding target image data set. In this way, the high-resolution of digital non-medical photographs or images can be leveraged for the enhancement or correction of medical images, and the trained neural network can be used to reduce noise and image artifacts in medical images acquired by the medical imaging modality.

Abstract: Smart electricity monitors with unique identities placed at individual power outlets within a building communicate frequent power measurements to a service which determines, from this power usage data, which outlets are associated with occupied seats within the building. This occupancy information can be used to update an occupancy model for the building that is used to forecast the building's occupancy. Based on present occupancy, projected occupancy, and other data, in some instances, the building's thermostats can be controlled and unoccupied seats can be assigned dynamically.

Abstract: Apparatus and methods are provided for graphically defining a real-world cybersecurity protocol of an entity. The graphical platform includes searchable, manipulatable, graphs mapping cybersecurity threats. Manipulating nodes and relationships within the graphs translates into real-time modification of a cybersecurity protocol in effect for the entity. An ability to map known cybersecurity threats and analyze them (even according to known frameworks) may streamline and integrate efforts of cybersecurity defense teams. Graphical representation of a security protocol facilitates proactive threat hunting as well as expediting incident response activities by providing evidence-based pathways to inform impact analysis and source event analysis.

Abstract: Methods and apparatuses are described for analyzing unstructured computer text for domain-specific stopword identification and removal. A computer data store stores unstructured text. A server computing device splits the unstructured text into phrases and generates tokens from the phrases. The server computing device generates a set of bootstrap keywords using the tokens. An artificial intelligence neural network executing on the server computing device generates a stopword training model. The server computing device generates a first set of candidate stopwords using the bootstrap keywords and the stopword training model. The server computing device generates regular expressions using the bootstrap keywords, and generates a second set of candidate stopwords using the regular expressions. The server computing device stores the candidate stopwords in the data store, and removes stopwords from the unstructured text using the data store.

Abstract: A technique for calculating a semantic distance between subjects includes performing a mathematical operation between each of one or more first topic vectors and each of one or more second topic vectors to generate respective strength values. The first topic vectors are associated with respective first topics of a first subject, the second topic vectors are associated with respective second topics of a second subject, and the respective strength values are indicative of a relative closeness between associated ones of the first and second topics. Relevant ones of the respective strength values are summed to provide an overall strength value between the first subject and the second subject. A semantic distance between the first subject and the second subject is determined based on the overall strength value.

Abstract: This disclosure relates to system and method for generating an optimized result set based on vector based relative importance measure (VRIM). In one embodiment, the method comprises determining a vector representation for each of a plurality of input keywords extracted from an input query, and determining a plurality of representative keywords corresponding to the plurality of input keywords from a keyword database based on the vector representation for each of the plurality of input keywords and a vector representation for each of a plurality of keywords in the keyword database. The method further comprises determining a score for a plurality of response candidates corresponding to the input query based on a relative importance score and a similarity score for each of the plurality of representative keywords present in the plurality of response candidates, and generating a result set from the plurality of response candidates based on the score.

Abstract: An expected value of a measurement in a first context may be inferred based at least partly on a contextual signal. The contextual signal may comprise an actual value that is: (i) of a same type as the expected value, and (ii) associated with a second context that is different from the first context (e.g., the contexts can comprise geographical areas), or the contextual signal may comprise an actual value that is: (i) of a different type than a type of the expected value, and (ii) associated with the first context, or a second context that is different from the first context. If a difference between the expected value and an actual value of the first context is greater than a threshold difference, this condition is considered an anomaly. A detected anomaly may be used to determine an event that may be significant or otherwise of interest to a user community.

Abstract: A multiplication device for performing a matrix-vector-multiplication may be provided. The multiplication device comprises a memristive crossbar array comprising a plurality of memristive devices. The device comprises a decomposition unit adapted for decomposing a matrix into a partial sum of multiple sub-matrices, and decomposing a vector into a sum of multiple sub-vectors, a programming unit adapted for programming the plurality of the memristive devices with values representing elements of the sub-matrices such that each one of the memristive devices corresponds to one of the elements of the sub-matrices, an applying unit adapted for applying elements of one of the multiple sub-vectors as input values to the memristive crossbar array to input lines of the memristive crossbar array resulting in partial results at output lines of the memristive crossbar array, and a summing unit adapted for scaling and summing the partial results building the product of the matrix and the vector.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for an adaptive oracle-trained learning framework for automatically building and maintaining models that are developed using machine learning algorithms. In embodiments, the framework leverages at least one oracle (e.g., a crowd) for automatic generation of high-quality training data to use in deriving a model. Once a model is trained, the framework monitors the performance of the model and, in embodiments, leverages active learning and the oracle to generate feedback about the changing data for modifying training data sets while maintaining data quality to enable incremental adaptation of the model.

Abstract: An approach is provided in which an information handling system deploys a container into a container group. The container includes service level information corresponding to a service level. A process executing within the container group analyzes the service level information against status data corresponding to the container group and performs a service-related action to enforce the service level within the container group based on the analysis.

Abstract: User behavior analysis method and device are disclosed. The method comprises: obtaining a behavior record of users regarding commodities; acquiring, by determining a commodity feature vector in a predetermined commodity feature space of each of the commodities, a commodity feature matrix consisting of the commodity feature vectors of the commodities; for each of the users, calculating, based on a sub behavior record of the corresponding user regarding each of the commodities, a preference score of the corresponding user regarding each of the commodities, so as to get a score matrix composed of the preference scores of the users regarding the commodities; and for each of the users, determining, based on a regularized least squares based solution of a difference function of the score matrix and a prediction matrix, a user feature vector of the corresponding user in its related predetermined commodity feature space.

Abstract: The present invention may include an embodiment identifies the plurality of source code. The embodiment may extract one or more characteristics from a plurality of lines of the plurality of source code. The embodiment may analyze the one or more extracted characteristics for an inclusion relation and a congruent relation. The embodiment may generate a plurality of node relations of a plurality of nodes based on the inclusion relation and the congruent relation, where each node within the plurality of nodes corresponds to each line of the plurality of source code. The embodiment may determine a sum of the one or more nodes from the plurality of nodes that have no inclusion relation based on the analyzed inclusion relation and the analyzed congruent relation and the embodiment may display the sum of the determined one or more nodes.

Abstract: An alertness prediction bio-mathematical model for use in devices such as a wearable device that improves upon previous models of predicting fatigue and alertness by gathering data from the individual being monitored to create a more accurate estimation of alertness levels. The bio-mathematical model may be a two-process algorithm which incorporates a sleep-wake homeostasis aspect and a circadian rhythm aspect. The sleep-wake homeostasis aspect of the model is improved by using actigraphy measures in conjunction with distal skin, ambient light and heart rate measures to improve the accuracy of the sleep and wake estimations. The circadian rhythm model aspect improves fatigue prediction and estimation by using distal skin, heart rate and actigraphy data. The sleep-wake homeostasis and circadian rhythm aspects may also be combined with additional objective and subjective measures as well as information from a user to improve the accuracy of the alertness estimation even further.

Abstract: This document generally describes systems, methods, devices, and other techniques related to speaker verification, including (i) training a neural network for a speaker verification model, (ii) enrolling users at a client device, and (iii) verifying identities of users based on characteristics of the users' voices. Some implementations include a computer-implemented method. The method can include receiving, at a computing device, data that characterizes an utterance of a user of the computing device. A speaker representation can be generated, at the computing device, for the utterance using a neural network on the computing device. The neural network can be trained based on a plurality of training samples that each: (i) include data that characterizes a first utterance and data that characterizes one or more second utterances, and (ii) are labeled as a matching speakers sample or a non-matching speakers sample.

Abstract: Debugging systems are configured to resolve both memory aliasing conditions in which a write instruction is directed to an unknown destination address, and concurrency conditions in which control flow information is collected for multiple, concurrently executing threads. Recorded state values corresponding to an application's prior execution and control flow information are both obtained.

Abstract: There is provided an information processing apparatus including: a sensor data generator sensing a user behavior and generating sensor data corresponding to the user behavior; a behavior recognizing unit performing a predetermined threshold value process on the sensor data to recognize the behavior exhibited by the user and generating behavior information that is information indicating the behavior exhibited by the user; a behavior manager managing the behavior information generated by the behavior recognizing unit in correspondence with the time point at which the behavior corresponding to the behavior information is exhibited; and a behavior information post-processing unit performing a predetermined post-process on the behavior information managed by the behavior manager, wherein the behavior recognizing unit further includes a plurality of behavior determination units specified to specific behaviors exhibited by the user and generates the behavior information based on the determination results of the plur

Abstract: In one embodiment, a current context of a mobile device may be ascertained, where the current context includes an indication of a last application opened via the mobile device, wherein the last application opened is one of a plurality of applications installed on the mobile device. A probability, for each of the plurality of applications, that a user of the mobile device will use the corresponding application under the current context may be determined, where the probability for at least a portion of the plurality of applications is determined by applying a computer-generated model to the current context. One or more of the plurality of the applications may be identified based, at least in part, upon the probability, for each one of the plurality of applications, that the user of the mobile device will use the corresponding application.

Abstract: Techniques for determining connected components of a graph via incremental graph analysis algorithms are provided. In one example, a computer-implemented method comprises analyzing, by a system operatively coupled to a processor, a first differential value representing an initial incremental difference of elements between selected initial elements of an initial vector and selected input elements of an input vector associated with a graph. The method further comprises recurringly analyzing, by the system, a second differential value representing a subsequent incremental difference of elements between selected updated elements of an updated initial vector and selected additional elements of another input vector associated with the graph until the second differential value is zero.

Abstract: Systems and methods for tuning hyperparameters of a model includes: receiving at a remote tuning service a multi-criteria tuning work request for tuning hyperparameters of the model of a subscriber, wherein the multi-criteria tuning work request includes: a first objective function of the model to be optimized by the remote tuning service; a second objective function to be optimized by the remote tuning service, the second objective function being distinct from the first objective function; computing a first conditionally constrained joint function for the model based on subjecting the first objective function to the second objective function; a second conditionally constrained joint function for the model based on subjecting the second objective function to the first objective function of the model; executing a tuning operation of the hyperparameters for the model; and identifying proposed hyperparameter values based on one or more hyperparameter-based points along a non-convex Pareto optimal curve.

Abstract: Original data that represents a real-world object or activity and organized along three or more dimensions is received. The original data is represented as a product of several multipliers including a sparse core, such that the sparse core has fewer non-zero values than a tensor representation of the original data, and one or more unitary matrix multipliers. Modified data is generated based on the original data using the multipliers. This includes compressing, or reconstructing missing elements in, the tensor representation of the original data, such that the modified data provides a description of the real-world object or activity that is less complete or more complete, respectively, relative to the original data.

Abstract: A method and an apparatus for detecting changed data are provided. The method includes: recording change status information of each data field in a data table during a version change; obtaining a probability of a second data field being changed simultaneously when a first data field is changed, the probability being a confidence probability of the second data field being changed in the first data field being changed; determining, when confidence probabilities of at least two second data fields being changed in the same first data field being changed are greater than a confidence probability threshold of the data table, that the second data fields corresponding to the confidence probabilities greater than the confidence probability threshold are a combined field; determining whether a combination of the changed data fields matches a combined field template during the current version change, and prompting a location of the unmatched changed data field.

Abstract: Provided are a computer program product, system, and method for processing images from a gaze tracking device to provide location information for tracked entities. A gazed image is received from a gaze tracking device at which the user is gazing while operating the gaze tracking device. User speech is received from a microphone. The user speech is as having memorization intent. A description of a tracked entity that is an object of the memorization intent is extracted from the user speech. A tracking object in a tracking object database is created including the gazed image and the extracted description of the tracked entity for which the user has memorization intent. A determination is made of a location of the tracked entity and the determined location of the tracked entity is associated with the created tracking object in the tracking object database.

Abstract: Systems and methods for tuning hyperparameters of a model includes: receiving a multi-criteria tuning work request for tuning hyperparameters of the model of the subscriber to the remote tuning service, wherein the multi-criteria tuning work request includes: a first objective function of the model to be optimized by the remote tuning service; a second objective function to be optimized by the remote tuning service, the second objective function being distinct from the first objective function; computing a joint tuning function based on a combination of the first objective function and the second objective function; executing a tuning operation of the hyperparameters for the model based on a tuning of the joint function; and identifying one or more proposed hyperparameter values based on one or more hyperparameter-based points along a convex Pareto optimal curve.

Abstract: Techniques described and suggested herein include various systems and methods for detecting privilege escalation vulnerabilities. A detection service may determine one or more resources of a service or application the computing resource service provider to test. The detection service may determine a set of message digests corresponding to responses to requests to access the resource of the service or application. A subset of the set of message digests associated with an identifier may be compared to determine whether an entity associated with one of the subset of message digests is able to exceed a set of privileges attributed to the entity.

Abstract: A light fixture includes a housing having a first side and a second side opposite the first side. The light fixture further includes a first light board coupled to the first side. The first light board has at least one light emitting element emitting a first light output in a first direction. The light fixture further includes a second light board coupled to the second side. The second light board has at least one light emitting element emitting a second light output in a second direction. The light fixture further includes a first driver supported by the housing and in electrical communication with the first light board. The first driver is operable to control the first light output. The light fixture further includes a second driver supported by the housing and in electrical communication with the second light board. The second driver is operable to control the second light output.

Abstract: Systems and method implemented through a distributed security system for determining and addressing risk of users, groups of users, locations, and/or companies include obtaining log data from the distributed security system; analyzing the log data to obtain a risk score for an entity associated with the distributed security system, wherein the entity comprises one of a user, a group of users, a location, and a company, and wherein the risk score is a weighted combination of pre-infection behavior, post-infection behavior, and suspicious behavior; performing one or more remedial actions for the entity; and subsequently obtaining updated log data and analyzing the updated log data to obtain an updated risk score to determine efficacy of the one or more remedial actions.

Abstract: A building management system (BMS) includes a plurality of devices controllers, a BMS database, and a BMS controller. The device controllers are configured to monitor and control one or more HVAC devices and to store and process time-series data associated with the HVAC devices. The BMS database is configured to store a master-index, the master-index identifying the time-series data stored by each of the device controllers. The BMS controller is configured to receive a processing request that requires the time-series data stored by one or more of the device controllers. The BMS controller is further configured to generate one or more processing sub-requests. The device controllers are further configured to handle the processing sub-requests and provide processing results to the BMS controller. The BMS controller is further configured to combine processing results from the device controllers.

Abstract: A method, system, and computer program product for selecting forecasting model complexity using eigenvalues are provided in the illustrative embodiments A process is represented in a model. The model comprises a mathematical representation of the process in a certain degree. A first portion of historical data generated by the process during a first period is selected and includes an actual value of an outcome of the process and a value of a feature influencing the process during the first period. A prediction is made of a predicted value of the outcome. A difference between the prediction and the actual value of the outcome is determined. The difference is represented as a change in a distribution of eigenvalues. According to the change, a second model is to represent the process. The second model comprises a second mathematical representation of the process in a different degree.

Abstract: A method for security and/or automation systems is described. In some embodiments, the method may include detecting departure data relating to at least one of an occupant and a premises, identifying a likelihood of an absence in relation to the premises based at least in part on the departure data, and sending a confirmation request based at least in part on the identifying for the occupant associated with the premises to confirm the absence. In some cases, the absence may include the premises being unoccupied for at least a portion of a predetermined time period. The predetermined time period may include at least one night.

Abstract: Aspects of the present disclosure are directed to electronic computer implemented methods of data communication. The method includes receiving a plurality of virtual nodes with EDI data payload including a node attribute, a GPS location attribute and a biometric ID attribute and at least one data element associated with the GPS location attribute. In further aspect, the method includes electronically via a data communications network, processing the EDI data payloads including the node attribute, the GPS location attribute and the biometric ID attribute and the at least one data element associated with the GPS location attribute. Then, electronically processing the EDI data payloads in a network with machine learning and providing an electronic message responsive thereto. In yet a further aspect, the data communications method includes a step of processing the smart data set in the network with machine learning and providing an electronic message responsive thereto.

Abstract: Systems, methods, and non-transitory computer-readable media can determine an event that may affect at least one activity being performed by a first group of users through the computing system. A set of first measurements of the at least one activity being performed by the first group of users over a period of time are determined. A set of second measurements of the at least one activity for the first group of users over the period of time are generated, wherein the set of second measurements are predicted based at least in part on a machine learning model. Data describing an impact of the event on the first group of users is generated based at least in part on the set of first measurements and the set of second measurements.

Abstract: A system, method and computer program product performs data analysis and clustering. A plurality of data objects are received, each represented by a vector of features and associated with a point in time. The plurality of data objects is divided into first time slices to form a plurality of consecutive sets of data objects. Each set of data objects is sub-divided into one or more second time slices so as to form one or more subsets of data objects. The data objects in each set and subset of data objects are processed to derive clusters of data objects according to similarity of features. The clusters of data objects from different sets and subsets of data objects are used to detect changes in the relevance of cluster features over time.

Abstract: Embodiments of the present invention generally relate to the modeling of building occupant behavior. An embodiment of the present invention relates to a method for generating interdependent schedules in an occupant simulation model. Within schedules generated in accordance with the present invention may be included activities that require the attendance of other occupants. Disclosed are methods for distributing multi-occupant activities among other occupants of a building. In another embodiment of the present invention, personas are used to generate simulated occupant schedules from a limited number of real-world occupant surveys. Characteristics of the real-world schedules are manipulated so as to fit desired characteristics. Multiple persona models are disclosed.

Abstract: Improved systems and methods for automatically discovering and filtering electronic messages. These systems and methods improve the operation of computer apparatus to achieve dramatic reductions in processing resources, data storage resources, network resources, and filter production times compared to conventional approaches. In some examples, improvements result from configuring computer apparatus to perform a unique sequence of specific electronic message processing rules in a network communications environment. In this regard, these examples are able to automatically learn the structures and semantics of machine generated electronic message headers, accelerating the ability to support new message sources and new markets. These examples provide a purchase related electronic message discovery and filtering service that is able to identify and filter purchase related electronic messages with high accuracy across a wide variety of electronic message formats.

Abstract: A method and system obtaining positioning data from an object traveling on a plurality of routes; mapping the data into a plurality of points on a digital map; identifying points that are matched based on a distance and having a traveling direction consistent with the route directions; obtaining candidate transition points from the plurality of points on the digital map; aggregating the candidate transition points by applying a clustering algorithm to obtain a first cluster of points and a plurality of second clusters of points, verifying a confidence that the first cluster of points are transition points indicating a transition between the routes, and in response to the confidence being below a threshold confidence, classifying the first cluster of points as a first plurality of traveling points having a first direction and automatically adjusting the digital map at least in part based on the first cluster of points.

Abstract: In one embodiment, a supervisory device in a network identifies a workaround for a symptom exhibited by a particular device in the network by traversing a path in a symptom-problem graph. The path in the graph includes a symptom vertex that represents the symptom, a problem vertex that shares an edge with the symptom vertex and represents a problem that causes the symptom, and a workaround vertex that shares an edge with the problem vertex and represents the workaround for the symptom. The supervisory device causes the identified workaround to be applied to the particular device and updates the path in the graph to replace the workaround vertex with a fix vertex that represents a fix for the problem that causes the symptom exhibited by the particular device. The supervisory device causes the fix to be applied to the particular device based on the updated path in the graph.

Abstract: A multi-token matching scheme efficiently detects multi-token keyword matches in content received by a database system. A keyword list may include multi-token keywords. Head tokens from the multi-token keywords are converted into a head hashmap. Token chains identifying tokens and delimiters in the multi-token keywords are generated and linked to the head hashmap. Tokens in the content are compared with the head hashmap. The matching tokens and following segments in the content are compared with the token chains. Portions of the content matching the token chains are identified as keyword matches. The matching scheme may use reverse delimiter tries to detect content matches for multi-token keywords with leading delimiters.