Abstract: The disclosed embodiments illustrate a method and a system for controlling KPI parameters of a transportation system. The method includes extracting historical commuting characteristics of one or more commuters, from a database server over a communication network. The method further includes generating a predictive model based on the extracted historical commuting characteristics. The method further includes generating a service schedule of one or more transportation services of the transportation system. The service schedule of the one or more transportation services may be generated by use of the generated predictive model, based on defined criteria of the transportation system. The method further includes controlling a KPI parameter of the transportation system to attain a desired KPI parameter of the KPI parameter, based on the generated service schedule, when the one or more transportation services are deployed at one or more time stamps.

Abstract: Systems and methods for allocating resources in a transportation system are disclosed. A vehicle of the transportation system may be dispatched by one or more processors; first and second portions of the vehicle are allocated to first and second groups of users. A parameter of the first group of users may be tracked at a first location. Part of the second portion of the vehicle may be reallocated to the first group of users, in real-time, when the tracked parameter of the first group of users exceeds a threshold.

Abstract: A system and method for facilitating parking enforcement officer performance in real time with the aid of a digital computer is provided. A time-based active representational model of the city is created by fusing sensory data collected from various sources around a city with numerical data gleaned from historical and on-going activities, including parking regulation citation and warning numbers, resource allocations, and so on. The model can be used to form quantitative predictions of expected violations, revenue stream, and so forth, that can then be used as recommendations as to where to enforce and when, so as to maximize the utilization of the limited resources represented by the officers on the street. Moreover, the performance of the officers can be weighed against expectations of performance postulated from the quantitative predictions.

Abstract: A method and system for image processing of an image captured by an image-capturing device, can involve dividing a region of interest in an image into blocks, wherein the image is captured by an image-capturing device. The region of interest can be associated with one or more faces in the image, and a resulting division of the dividing of the region of interest may be non-uniform. A minimum block size can be defined and the coordinates of each block after dividing of the region of interest in the image into the blocks. The one or more faces in the image can be redacted and reconstructed using the coordinates. In addition, redacting and reconstructing of the image is reversible for use with detecting the one or more faces in the image. The coordinates can specify top left coordinates and bottom right coordinates of each block.

Abstract: A system processes a set of images to verify whether the images depict a type of property damage, such as hail damage from a hail storm. The system receives digital images of a roof or other facet of the property that were taken by an image capturing device after the storm event. The system also receives storm data relating to the storm event and claim data related to a property damage claim. The system uses the claim data to process the digital images to automatically identify damage to the roof or other facet. The system then analyzes the storm data to determine whether the damage is consistent with an actual, naturally-occurring storm event, and it generates a report depicting its findings.

Abstract: A system and method for monitoring parking enforcement officer performance with the aid of a digital computer is provided. A time-based active representational model of the city is created by fusing sensory data collected from various sources around a city with numerical data gleaned from historical and on-going activities, including parking regulation citation and warning numbers, resource allocations, and so on. The model can be used to form quantitative predictions of expected violations, revenue stream, and so forth, that can then be used as recommendations as to where to enforce and when, so as to maximize the utilization of the limited resources represented by the officers on the street. Moreover, the performance of the officers can be weighed against expectations of performance postulated from the quantitative predictions.

Abstract: A system for managing fare collection in a public transit network may include a fare collection system and a fare management system. The fare collection may collect fare information from passengers boarding or getting off a vehicle. The fare management system may use the received fare information to determine a fare return series over a period of time, which includes multiple fare returns. The fare management system may use a hierarchical cluster method to cluster multiple fare returns into one or more clusters represented in a dendrogram, apply a “silhouette” method to the dendrogram and obtain an optimal clustering. The system may further output a graphic representation, such as a trend line, for each of the clusters over a time period. Based on the clustering results, the system may also deploy a revised fare class schedule or transit system schedule to the fare collection system.

Abstract: A method and apparatus for detecting an error in a business process via an exchange of email messages. In one example, the method may be executed by a processor of a business process analysis server (BPAS). For example, the method includes receiving an email, wherein the email includes an address of the BPAS, analyzing the email to determine at least one feature, determining the business process based on the at least one feature, determining one or more variables that is associated with the business process, detecting the error in the business process associated with the email based on at least variable of the one or more variables associated with the business process and generating an alert email in response to the error that is detected, wherein the alert email requests a correction to the at least one variable to complete the business process.

Abstract: System and methods for personalizing an application include an application server for hosting an application that is executed on a wireless device, the server being configured to monitor usage of a user interface including an interaction between a user of the application and one or more user interface elements and present an updated user interface comprising one or more additional user-interface elements based on the usage.

Abstract: The disclosed embodiments illustrate a method and a system for managing crowd-sensed data, associated with events occurring in a geographical area. The method includes receiving crowd-sensed data from one or more data sources, wherein the crowd-sensed data comprises one or more event reports associated with at least a type of each of one or more events reported by the one or more data sources. The method further includes generating a data structure based on an aggregation of the received crowd-sensed data. Further, the method includes determining first information and second information based on at least the generated data structure, a reputation score of each of the one or more data sources and metadata associated with each of the one or more event reports. The method further includes displaying at least the determined first information and second information based on at least a prioritization of the one or more events.

Abstract: According to embodiments illustrated herein, there is provided a system for predicting a health condition of a patient. The system further includes one or more processors configured to separately cluster data points from a set of medical records associated with a first class of patients and a second class of patients. A similarity value of each of the clustered data points with respect to a pre-selected subset of data points that represents landmark points may be determined, using a parameterized similarity measure. One or more classifiers are trained using the determined similarity value of each data point. The trained one or more classifiers are adapted to learn one or more parameters of the parameterized similarity measure during the training. An occurrence of the health condition of the patient may be predicted based on the trained one or more classifiers and one or more medical records of the patient.

Abstract: Neural network systems are provided that comprise one or more neural networks. The first neural network can comprise a convolutional neural network (CNN) long short-term memory (LSTM) architecture for receiving a primary data set comprising text messages and output a primary data structure comprising a text pattern-based feature. The second neural network can comprise a CNN architecture for receiving a secondary data sets derived from the primary data set and output a plurality of secondary data structures. The third neural network can combine the data structures to produce a combined data structure, and then process it to produce a categorized data structure comprising the text messages assigned to targets. The primary data set can comprise hate speech and the categorized data structure can comprise target categories, for example, hate targets. Methods of operating neural network systems and computer program products for performing such methods are also provided.

Abstract: An automated training method enhances conversational effectiveness of a system participant interacting with a virtual client. The method operates on received speech input to produce text. A speech effectiveness analysis processor receives both the speech and text and produces quantified metrics measuring speech effectiveness based on the input parameters. Personalized feedback is generated based on the quantified metrics and is transmitted to the system participant. A virtual client dialog is generated using a dynamic simulation processor. The virtual client dialog is based on the quantified metrics, such that the virtual client responds to the speech input using the generated dialog.

Abstract: According to exemplary methods, automated image processing of images from order locations of a multi-lane drive-thru system is continually performed using an automated ordering system. It is automatically determined if a vehicle is present at any of the order locations based on the automated image processing. It is automatically determined if an order has been initiated into the automated ordering system. When the order has been initiated while the vehicle is present at any of the order locations, an automated sequencing processing is automatically begun. The automated sequencing processing determines an order pick-up sequence for picking up orders at pickup windows of the multi-lane drive-thru system. When the order has been initiated while the vehicle is not present at any of the order locations, a potential out-of-sequence warning is automatically associated with the order and output from the automated ordering system.

Abstract: A system and method for facilitating parking enforcement officer dispatching in real time with the aid of a digital computer is provided. A time-based active representational model of the city is created by fusing sensory data collected from various sources around a city with numerical data gleaned from historical and on-going activities, including parking regulation citation and warning numbers, resource allocations, and so on. The model can be used to form quantitative predictions of expected violations, revenue stream, and so forth, that can then be used as recommendations as to where to enforce and when, so as to maximize the utilization of the limited resources represented by the officers on the street. Moreover, the performance of the officers can be weighed against expectations of performance postulated from the quantitative predictions.

Abstract: A system segments a set of images of a property to identify a type of damage to the property. The system receives, from an image capturing device, a digital image of a roof or other feature of the property. The system processes the image to identify a set of segments, in which each segment corresponds to a piece of the feature, such as a tab or tooth of a shingle on the roof. The system saves a result of the processing to a data file as a segmented image of the property, and it uses the segmented image to identify a type of damage to the property.

Abstract: A neural network is used to model to model the joint distribution of attributes across multiple health surveys. These multiple health surveys include large scale survey datasets and small scale survey datasets. The neural network model is trained using a combined dataset of the large scale survey datasets and the small scale survey datasets. The large scale survey datasets and the small scale survey datasets may include missing value indicators. The joint distribution of attributes modeled by the neural network model are the used to impute substitute values for the missing values to thereby create an output large scale dataset that does not include missing values.

Abstract: According to exemplary methods of training a convolutional neural network, input images are received into a computerized device having an image processor. The image processor evaluates the input images using first convolutional layers. The number of first convolutional layers is based on a first size for the input images. Each layer of the first convolutional layers receives layer input signals comprising features of the input images and generates layer output signals that include signals from the input images and ones of the layer output signals from previous layers within the first convolutional layers. Responsive to an input image being a second size larger than the first size, additional convolutional layers are added to the convolutional neural network. The number of additional convolutional layers is based on the second size in relation to the first size. The additional convolutional layers are initialized using weights from the first convolutional layers.

Abstract: The disclosed embodiments illustrate methods and systems for predicting demand of vehicles in a transportation network. The method includes determining demand events at each of one or more locations for time intervals based on historical demand data. The demand events correspond to a demand of vehicles at one or more locations during plurality of time intervals throughout a day. The method includes creating a graph comprising nodes, and edges connecting nodes, each node being representative of a demand event from demand events. An edge is representative of dependency between two demand events from demand events. The method includes predicting demand of vehicles at a location from one or more locations during a predetermined time interval based on the graph and a real time demand of vehicles associated with other demand event. The method includes displaying demand prediction on a computing device at one or more locations of transportation network.

Abstract: Neural network systems are provided that comprise one or more neural networks. The first neural network can comprise a convolutional neural network (CNN) long short-term memory (LSTM) architecture for receiving a primary data set comprising text messages and output a primary data structure comprising a text pattern-based feature. The second neural network can comprise a CNN architecture for receiving a secondary data sets derived from the primary data set and output a plurality of secondary data structures. The third neural network can combine the data structures to produce a combined data structure, and then process it to produce a categorized data structure comprising the text messages assigned to targets. The primary data set can comprise hate speech and the categorized data structure can comprise target categories, for example, hate targets. Methods of operating neural network systems and computer program products for performing such methods are also provided.