Abstract: Techniques to verify a participant's visit to a specific location are described. An embodiment may provide a system that generates a pattern that is unique to the location, and that may further be unique to a date or time, a transaction, or other criteria. Participants may capture the pattern, for example, using a mobile device, and transmit the pattern to a verification system. The verification system may decode, translate, decrypt or otherwise obtain information from the pattern. The information obtained from the pattern may be used to verify that the pattern came from the location. The participant may then receive credit for the visit. Other embodiments are described and claimed.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for configuring an artificial neural network to a particular surveillance situation. A number of object classes characteristic for the surveillance situation are selected. The object classes form a subset of the total number of object classes for which the artificial neural network is trained. A database is accessed that includes activation frequency values for the neurons within the artificial neural network. The activation frequency values are a function of the object class. Those neurons having activation frequency values lower than a threshold value for the subset of selected object classes are removed from the artificial neural network.

Abstract: Methods and systems are described for selecting a level of autonomy. According to at least one embodiment, a method for selecting a level of autonomy includes determining a first likely condition of a first user of a task and/or building system, selecting a first autonomy level from multiple levels based at least in part on the first likely condition, and making a first decision in the task and/or the building system based on the first autonomy level.

Abstract: Techniques are described for automated selection of components for a generalized linear model. In one example, a method includes determining a candidate set of distributions, a candidate set of link functions, and a candidate set of predictor variables, based at least in part on a dataset of interest. The method further includes selecting a distribution from the initial candidate set of distributions and a link function from the initial candidate set of link functions, based at least in part on the candidate set of predictor variables; and selecting predictor variables from the candidate set of predictor variables, based at least in part on the selected distribution and the selected link function. The method further includes reiterating the selecting processes until a stopping criterion is fulfilled, and generating a generalized linear model output comprising the selected distribution, the selected link function, and the selected predictor variables.

Abstract: The present disclosure is directed to the generation of a compact artificial neural network by removing individual nodes from the artificial neural network. Individual nodes of the artificial neural network may be deactivated randomly and/or selectively during training of the artificial neural network. In some embodiments, a particular node may be randomly deactivated approximately half of the time during processing of a set of training data inputs. Based on the accuracy of the results obtained when the node is deactivated compared to the accuracy of the results obtained when the node is activated, an activation probability may be generated. Nodes can then be selectively removed from the artificial neural network based on the activation probability.

Abstract: A method and apparatus for detecting an abnormal event related to a person at home are provided. The method includes measuring home electricity use data in each first predetermined time interval, determining whether an abnormal event related to the person at home has occurred based on the home electricity use data, generating a notification message including the abnormal event related to the person at home when it is determined that the abnormal event related to the person at home has occurred, and transmitting the notification message to a predetermined user device.

Abstract: A human machine interface system and method of operating the human machine interface system are provided. The system includes a voice user interface module with an audio input unit to receive a voice signal from a microphone and digitize the voice signal as an audio file. A recognition engine unit is coupled to the audio input unit and is configured to analyze the audio file and recognize a plurality of voice commands and output a plurality of recognized commands. A logger unit is coupled to the recognition engine unit to receive the plurality of recognized commands and output a user intent output and a normalized vector representation based on the plurality of recognized commands. A classification component receives the normalized vector representation from the logger unit and outputs a classified user profile to a plurality of human machine interface applications based on the normalized vector representation.

Abstract: The embodiments recite systems and methods that improve the traditional underwriting process within a financial institution. These embodiments produce an underwriting model that emulates the resolution patterns of top performing underwriters. The underwriting model once is built and tested is incorporated into decision tools that provide underwriters with insightful advices when underwriting a client. The embodiments use statistical learning techniques such as support vector machine and logistic regression. These techniques can assume a linear or nonlinear relationship between factors and risk classes. Furthermore, the underwriting model also uses artificial intelligence tools such as expert systems and fuzzy logic. A company's underwriting standards and best underwriting practices may be updated periodically so that underwriting model based on decision heuristic keep improving the quality of its output over time.

Abstract: A neural network learning device includes a learning unit configured to perform a learning process that learns a neural network using a plurality of learning data sets. The learning process includes a data reading process, a synaptic weight correction process, a neuron generation process, and a neuron removal process. The neural network learning device includes an intermediate layer generation unit configured to perform an intermediate layer generation process. The intermediate layer generation process newly generates an additional intermediate layer including at least one neuron as an intermediate layer of the neural network when a loss function is greater than a predetermined second threshold value after the learning process is performed. When the additional intermediate layer is newly generated by the intermediate layer generation process, the learning unit performs the learning process again, using the neural network in which the additional intermediate layer has been generated.

Abstract: Creating a note on a lock screen may be provided. In some examples, an application, such as a communication application, may detect an input to generate a note on a lock screen. The communication application may provide a note interface to capture the note. Next, the note may be captured on the note interface. In response, the note may be saved at a first storage location. The first storage location may be isolated by an operating system of the computing device to prevent an execution of a malicious code.

Abstract: A processing system having a function for appropriately maintaining processing accuracy, without depending on a degree of abrasion of a tool. An unused tool used in a machine tool is captured by using a camera mounted on a robot, so as to obtain a reference image of the tool. Next, an image of the tool is captured after a predetermined number of processing operations carried out. An average amount of tool abrasion per one processing operation is calculated based on the two images, so as to estimate a current amount of tool abrasion. Based on the estimation result and a previously input limit amount of abrasion, the number of remaining possible processing operations of the tool is calculated and output.

Abstract: Example implementations disclosed herein are directed to monitoring communications networks in real time to ensure that they are performing in a desired fashion. Metrics such as utilization and latency are monitored to ensure that customer SLAs (Service Level Agreements) are met in a timely fashion. To detect problems in advance, example implementations predict the future values of metrics and detect events if the future value violates certain conditions. Example implementations build models that can predict the future values of metrics and analyze historical, near real time and real time streaming data so as to build predictive models.

Abstract: A network lookup engine in a network switch is configured to generate multiple lookup queries for each incoming packet in parallel to a remote search engine. The number and type of the lookup queries depend on the protocols supported by the network switch. The responses from the search engine arriving at the lookup engine are not in the same order as the order of the packets. The network lookup engine is configured to collect the responses for the parallel lookup queries in two modes: 1) in-order mode in which the first packet having its lookup queries sent to the search engine has its responses collected first regardless of the order of the responses received from the search engine; 2) out-of-order mode in which the first packet having complete responses to its lookup queries from the search engine has its responses collected first regardless of the order of incoming packets.

Abstract: Embodiments relate to facilitating a meeting. A method for reducing an amount of communications to analyze in order to determine a cognitive state of an entity is provided. The method determines a first likelihood of an entity to have a particular cognitive state based on a set of physiological measures of the entity. The method receives communications from the entity. The method generates a graph of communications of the entity. The method performs a graphical text analysis on the graph to determine a second likelihood of the entity to have the particular cognitive state. The method determines whether the entity has the particular cognitive state based on the first likelihood and the second likelihood.

Abstract: The cost of data-mining is estimated where data-mining services are delivered via a distributed computing system environment. System requirements are estimated for a particular data-mining task for an input data set having specified properties. Estimating system requirements includes applying a partial learning tool to operate on sample data from the input data set.

Abstract: Embodiments relate to facilitating a meeting. A method for reducing an amount of communications to analyze in order to determine a cognitive state of an entity is provided. The method determines a first likelihood of an entity to have a particular cognitive state based on a set of physiological measures of the entity. The method receives communications from the entity. The method generates a graph of communications of the entity. The method performs a graphical text analysis on the graph to determine a second likelihood of the entity to have the particular cognitive state. The method determines whether the entity has the particular cognitive state based on the first likelihood and the second likelihood.

Abstract: Computerized methods and systems, including computer programs encoded on a computer storage medium, may detect event shown within digital video content captured by one or more video cameras, and correlate these detected events to real-world conditions that may not be captured within the digital video data. For example, a computing system may detect events shown within digital video content captured by one or more video cameras, and may obtain data that identifies at least one external event. The computer system may establish a predictive model that correlates values of event parameters that characterize the detected and external events during a first time period, and may apply the predictive model to an event parameter that characterizes an additional event detected during a second time period. Based on an outcome of the predictive model, the computing system may determine an expected value of the external event parameter during the second time period.

Abstract: A process for dynamically controlling spectrum access includes: collecting occupancy data; applying nonparametric estimation to the occupancy data; determining a cumulative hazard function; receiving a service request to occupy the wireless communication channel from a secondary user; accepting the service request from the secondary user if the wireless communication channel is unoccupied by the primary user; calculating a maximum duration for which the secondary user may occupy the wireless communication channel; determining a probability that a remaining idle time of the primary user is greater than an occupation time of the wireless communication channel of the secondary user; granting access to the secondary user for occupation of the wireless communication channel if the probability is greater than a selected probability threshold; and denying access to the secondary user for occupation of the wireless communication channel if the probability is less than the selected probability threshold to dynamically

Abstract: A malfunction diagnosing apparatus includes circuitry that collects vibration data generated when a diagnosed apparatus operates in a predetermined operating sequence, analyzes the collected vibration data and calculate a feature quantity, determines, based on the calculated feature quantity, whether or not something is wrong with the diagnosed apparatus, controls, if it is determined that something wrong with the diagnosed apparatus, the diagnosed apparatus to repeat the operating sequence that it is determined that something is wrong with the diagnosed apparatus for a predetermined number of times, and determines that malfunction occurs in the diagnosed apparatus if it is determined that something is wrong with the diagnosed apparatus in the operating sequence repeated for the predetermined number of times.

Abstract: An apparatus includes a processor to: assign a portion of currently available instruction-based processing resources to a first non-neuromorphic performance of an analytical function; in response to availability of sufficient remaining processing resources for a first neuromorphic performance of the analytical function with the same input values, assign a portion of the remaining processing resources to the first neuromorphic performance; analyze the output values generated by the first neuromorphic and non-neuromorphic performances to determine a degree of accuracy of the neural network in performing the analytical function; in response to at least the degree of accuracy exceeding a predetermined threshold, assign a portion of currently available processing resources to a second neuromorphic performance of the analytical function; and in response to availability of sufficient remaining processing resources for a second non-neuromorphic performance of the analytical function, assign a portion of the remaining

Abstract: Directed fragmentation of an unmanned aerial vehicle (UAV) is described. In one embodiment, the UAV includes various components, such one or more motors, batteries, sensors, a housing, casing or shell, and a payload for delivery. Additionally, the UAV includes a controller. The controller determines a flight path and controls a flight operation of the UAV. During the flight operation, the controller develops a release timing and a release location for one or more of the components based on the flight path, the flight conditions, and terrain topology information, among other factors. The controller can also detect a disruption in the flight operation of the UAV and, in response, direct fragmentation of one or more of the components apart from the UAV. In that way, a controlled, directed fragmentation of the UAV can be accomplished upon any disruption to the flight operation of the UAV.

Abstract: A system for linking corresponding patient information records is disclosed. A plurality of entities (1,1a) have respective patient databases comprising patient information records (3, 3a). Each entity (1, 1a) has associated therewith a patient identification algorithm (4,4a) for matching corresponding patient information records (3, 3a) of the same patient at different entities (1,1a). A linking subsystem (6) maintains a set of links (7) of a first entity (1) of the plurality of entities (1,1a). The linking subsystem (6) is arranged for linking patient information records (3) of the first entity (1) with corresponding patient information records (3a) of the other entities (1a). A link (ID, RID, RLoc) is established when a given patient information record (3) of the first entity (1) matches a corresponding patient information record (3a) of another entity (1a) based on the patient identification algorithm (4) of the first entity (1).

Abstract: A method for selecting a reduced number of model neurons in a neural network includes generating a first sparse set of non-zero decoding vectors. Each of the decoding vector is associated with a synapse between a first neuron layer and a second neuron layer. The method further includes implementing the neural network only with selected model neurons in the first neuron layer associated with the non-zero decoding vectors.

Abstract: A computer-implemented method and an apparatus for facilitating staffing of resources receives customer data corresponding to a plurality of customers of an enterprise. At least one intention is predicted for each customer to configure a plurality of intentions. An expected volume of interactions is estimated for at least one time period based on the plurality of intentions. Each interaction in the expected volume of interactions is associated with interaction attributes. Resource data corresponding to a plurality of resources of the enterprise is received. Each resource is associated with a plurality of resource attributes. At least one resource is mapped to each interaction based on a match between resource attributes associated with the at least one resource and the interaction attributes associated with the each interaction. A staffing of the plurality of resources is facilitated based on the mapping of the at least one resource to the each interaction.

Abstract: An online system, such as a social networking system, may determine user trends and identify actions to be taken by users that may help optimize revenue for an advertiser. A social networking system may generate offers for an advertiser based on the user trends, user characteristics, and claims about users where the offers include actions determined by the social networking system that users may take to help optimize revenue for advertiser. Venues may also sell ad space, ad inventory, and real-time customer data to advertisers through a social networking system.

Abstract: A method of processing a model of a telecommunications network, the telecommunications network. The method includes dividing the network elements of the telecommunications network into a plurality of different groups, for a first of the group of network elements being modelled, loading computer program code representing the group of network elements being modelled into the computer memory and executing the computer program to model the first group of network elements within a first time period. For a subsequent time period the first group of network elements is represented by the amount of network services consumed on the one or more interfaces with the second group of network elements determined within the first time period, while loading into the computer memory computer program code to model the second group of network elements.

Abstract: A method for customer-based outcome prediction that includes receiving recordings of interactions with customers in a customer group, analyzing the recordings of the interactions to generate interaction data, and building a predictive model using the interaction data, the predictive model identifying a variable relevant to predicting a likelihood of an identified outcome occurring in association with future interactions with customers in the customer group. The method also includes receiving a recording of a current interaction with a first customer, the first customer being in the customer group and analyzing the recording of the current interaction with the first customer to generate current interaction data. Further, the method includes adding the current interaction data to a first customer profile associated with the first customer and applying the predictive model to the first customer profile to predict the likelihood of the identified outcome occurring in association with the current interaction.

Abstract: A system and method for predicting the formulation and processing method and processing parameters for the formation of a biocomposite material is provided. The system and method utilizes the desired properties for the biocomposite material and utilizes these properties m a prediction system to determine the particular formulation, processing method and processing parameters for the formation of a biocomposite material having the desired characteristics. This information is output from the prediction system to a biocomposite material manufacturing system in order to form the biocomposite material and an end product formed therefrom that has the desired characteristics input into the prediction system.

Abstract: A graphical interface module may provide a set of graphical presentations comprising at least: a Likelihood of Delivery chart showing a probability distribution of predicted delivery dates; a Delivery Date Risk Trend chart showing how the completion time for the project predicted according to the Likelihood of Delivery chart has changed over time; and a Burndown chart that shows at least work-items of planned work for the project. Each of the Likelihood of Delivery chart, the Delivery Date Risk Trend chart, and the Burndown chart has a timeline axis.

Abstract: Provided is a process of modifying semantic similarity graphs representative of pair-wise similarity between documents in a corpus, the method comprising obtaining a semantic similarity graph that comprises more than 500 nodes and more than 1000 weighted edges, each node representing a document of a corpus, and each edge weight indicating an amount of similarity between a pair of documents corresponding to the respective nodes connected by the respective edge; obtaining an n-gram indicating that edge weights affected by the n-gram are to be increased or decreased; expanding the n-gram to produce a set of expansion n-grams; adjusting edge weights of edges between pairs of documents in which members of the expanded n-gram set co-occur.

Abstract: An online system using attributes of users to select content for presentation to the users predicts one or more attributes of users whose attributes are unavailable to the online system. For a user with one or more attributes unavailable to the online system, the online system applies a model to attributes of additional users to predict one or more attributes of the user. Attributes of the additional user use in the prediction may include demographic information and interactions with content by the additional users. The online system may determine an accuracy of the model by using the model to predict attributes for users whose attributes are known to the online system and comparing the predicted attributes to the known attributes. If the model's accuracy is less than a threshold value, the online system discontinues using the model to predict attributes of users.

Abstract: Among other things, requests are received from voice assistant devices expressed in accordance with different corresponding protocols of one or more voice assistant frameworks. Each of the requests represents a voiced input by a user to the corresponding voice assistant device. The received requests are re-expressed in accordance with a common request protocol. Based on the received requests, responses to the requests are expressed in accordance with a common response protocol. Each of the responses is re-expressed according to a protocol of the framework with respect to which the corresponding request was expressed. The responses are sent to the voice assistant devices for presentation to the users.

Abstract: Embodiments for monitoring groundwater discharge by one or more processors are described. A groundwater head at each of at least some of a plurality of locations is measured. A groundwater discharge for at least one of the plurality of locations is determined based on the measured groundwater heads. The determined groundwater discharge is compared to a groundwater discharge threshold associated with the at least one of the plurality of locations.

Abstract: A method for assisting game play. The method includes monitoring game play of the user playing a gaming application, wherein the user has a defined task to accomplish, wherein the task is associated with a task type. The method includes determining a task type proficiency rule for the task type based on results of a plurality of players taking on a plurality of tasks having the task type. The method includes determining a player proficiency score for accomplishing the task based on the task type proficiency rule. The method includes determining a user predictive rate of success in accomplishing the task based on the player proficiency score, the task type proficiency rule, and the task. The method includes determining a recommendation for the user based on the user predictive rate of success.

Abstract: A method for providing dispatching services for an on-demand transportation (ODT) service includes determining that a predictive assignment message should be transmitted to a vehicle, generating, in response to the determining that a predictive assignment should be transmitted to a vehicle, the predictive assignment message, and transmitting, to the vehicle, the predictive assignment message. Generating the predictive assignment message uses one or more prediction models computed from historical and real-time ODT service data.

Abstract: A method of transmitting data performed by a server through a network, a method of transmitting data in an ad hoc network, a server, and a data transmitting system are provided. The method of transmitting data performed by a server through a network includes receiving and storing data from a device of a first user; detecting that a device of a second user accesses the server; selecting data from the stored data based on user information; processing the selected data to be optimized for the device of the second user; and transmitting the processed data to the device of the second user, wherein the server is configured to combine data received from a plurality of devices of the first user and transmit the combined data to the device of the second user.

Abstract: Lossless compression of a content item using a neural network trained on content item cohorts. A computing system includes a neural network that is used to train a plurality of symbol prediction models. Each symbol prediction model is trained based on a corresponding cohort of content items. A particular symbol prediction model of the models trained is selected based on an intrinsic characteristic of a particular content item to be losslessly compressed such as, for example, the type or file extension of the content item. The content item is then losslessly compressed based on a set of symbol predictions fed to an arithmetic coder that are generated using the particular symbol prediction model selected.

Abstract: A method for assisting video content searches over a communication network by a user, comprising: A step of determining a first content item (CZ) by said user; A step of automatically extracting semantic data from this first content item; A step of automatically generating queries for at least one service, as a function of semantic data, making it possible to retrieve a set of videos on this service or these services; A step of presenting that set of video content to the user.

Abstract: A method and associated systems for automatic redistribution of virtual machines. A cloud-optimization module selects parameters, such as bandwidth requirements, that characterize an efficiency of a virtual network. It assigns weightings to these parameters based on relative importance of each parameter to the proper operation of the network, where the weightings may be determined as functions of captured network-performance statistics. The module translates the network's topology into a graph in which each node represents a network entity, such as a virtual machine or an application, and each edge represents a connection between two such entities. The module then uses a growing neural gas algorithm to revise the graph and the weightings, and translates the revised graph to a more optimal topology that has redistributed the network entities to operate more efficiently, as measured by the weighted parameters.

Abstract: Simulated network system and method having simulated user profiles and simulated item profiles representing items where the terms used to define characteristics of simulated user profiles and simulated item profiles are based on the same term vocabulary. Simulated sharing and spreading of item profiles by simulated user profiles arranged in the simulated network in a manner that gives a desired level of homophily to the network can produce information that can be presented to a real-world user of a real-world e-commerce system or other user network system, such as a Internet-based social network.

Abstract: Techniques for providing predictive metrics relating to employment positions are provided. A method may include receiving, by a computing device, data relating to a plurality of employment positions, wherein the data is received from a plurality of customers. The computing device may aggregate the data received from the plurality of customers and may determine statistics using the aggregated data, which are based on each of the plurality of employment positions. The computing device may generate one or more predictive metrics relating to the plurality of employment positions using one or more of the statistics.

Abstract: A method for customer-based outcome prediction that includes receiving recordings of interactions with customers in a customer group, analyzing the recordings of the interactions to generate interaction data, and building a predictive model using the interaction data, the predictive model identifying a variable relevant to predicting a likelihood of an identified outcome occurring in association with future interactions with customers in the customer group. The method also includes receiving a recording of a current interaction with a first customer, the first customer being in the customer group and analyzing the recording of the current interaction with the first customer to generate current interaction data. Further, the method includes adding the current interaction data to a first customer profile associated with the first customer and applying the predictive model to the first customer profile to predict the likelihood of the identified outcome occurring in association with the current interaction.

Abstract: A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.

Abstract: A computer-implemented system and method for detecting anomalies using sample-based rule identification is provided. Data for data is maintained analytics in a database. A set of anomaly rules is defined. A rare pattern in the data is statistically identified. The identified rare pattern is labeled as at least one of anomaly and non-anomaly based on verification by a domain expert. The set of anomaly rules is adjusted based on the labeled anomaly. Other anomalies in the data are detected and classified by applying the adjusted set of anomaly rules to the data.

Abstract: Certain embodiments involve generating or optimizing a neural network for risk assessment. The neural network can be generated using a relationship between various predictor variables and an outcome (e.g., a condition's presence or absence). The neural network can be used to determine a relationship between each of the predictor variables and a risk indicator. The neural network can be optimized by iteratively adjusting the neural network such that a monotonic relationship exists between each of the predictor variables and the risk indicator. The optimized neural network can be used both for accurately determining risk indicators using predictor variables and determining adverse action codes for the predictor variables, which indicate an effect or an amount of impact that a given predictor variable has on the risk indicator. The neural network can be used to generate adverse action codes upon which consumer behavior can be modified to improve the risk indicator score.

Abstract: A system and method for batched, supervised, in-situ machine learning classifier retraining for malware identification and model heterogeneity.

Abstract: An optical fiber sensor system and a method for determining a location of a disturbance having a signal processor with a plurality of activation cells adapted to react to components of a back-scattered signal and label the disturbance.

Abstract: An apparatus and a method to estimate a degree of user fatigue to video content are disclosed including a feature value calculating unit and a fatigue degree calculating unit. The feature value calculating unit is configured to calculate feature values corresponding to image and sound features in a video content. The fatigue degree calculating unit is configured to calculate a degree of user fatigue to the video content by applying the feature values to a fatigue degree estimation model.

Abstract: Digitized images of the propagated waveforms returned by Time-Domain (TD) sensors reveal the effects of bound water in soil samples, causing errors in the volumetric water content reported for samples due to soil type. Pattern analysis yields not only correct volumetric water content values but also the soil type from which the waveform was taken. Collection and analysis of waveforms from soils having precisely known properties yield computer-generated algorithms to improve the accuracy and number of reporting variables for integrated TD sensors. These algorithms can be deployed in the Cloud for external analysis, or can be incorporated within the sensor to report highly accurate readings of water content, conductivity and soil type under all conditions of these variables with no interpretive burden placed on the user of the sensor. Machine learning algorithms are intended to externally augment data measured by these sensors, or can be added to similar sensors.

Abstract: A first storlet engine is monitored for a first computational algorithm. The storlet engine includes a lookup table including a first characteristic of one or more nodes, a parser unit, and a resource availability monitor. The first computational algorithm is parsed into an abstract syntax tree using a parser unit. A first category of the first computational algorithm is determined by analyzing the abstract syntax tree. A first node of the lookup table with the first characteristic matching the first category for processing the first computational algorithm is identified based on the first category of the computational algorithm. The first computational algorithm is sent to the first node for processing.