Abstract: Technical solutions are described that address correcting input time-series data provided for analysis and predictions. An example computer-implemented method includes receiving, by a processor, a time-series data input by a user. The computer-implemented method also includes computing, by the processor, a first plurality of predicted values based on the time-series data input by the user; computing, by the processor, a second plurality of predicted values by. The computer-implemented method also includes determining estimated time-series data based on the time-series data input by the user. The computer-implemented method also includes computing the second plurality of predicted values based on the estimated time-series data.

Abstract: Disclosed is a computer-implemented method for recommending booths-to-visit to a user, comprising the following steps: accessing a user record, wherein the user record includes data indicative of booths which a user corresponding to the user record has visited in an exhibition; determining similarity level between records in a history database and the user record, the records in the history database including data indicative of the booths visited by previous visitors in the exhibition; selecting one of the records according to the determined similarity level; determining the booths in the selected record not yet visited by the user; obtaining the user's current location in the exhibition; determining a target booth, from the booths not yet visited by the user, by referring to the current location and a map database; and sending to the user a message indicative of the target booth.

Abstract: Disclosed is a computer-implemented method for recommending booths-to-visit to a user. The method includes: accessing a map database of an exhibition; accessing a history database that stores a plurality of records indicative of booths visited by a previous visitor in the exhibition; accessing a user record that includes data indicative of the booths which the user has visited; determining similarity level between each record in the history database and the user record, selecting one of the records according to the determined similarity level so as for the selected record to function as a reference record, usable to determine the booths not yet visited by the user; obtaining the user's current location in the exhibition, and determining a target booth, from the booths not yet visited by the user, by referring to the current location and the map database; and sending to the user a message indicative of the target booth.

Abstract: Individual health vulnerability is assessed by obtaining health risk prevalence level data containing health risk prevalence levels for one or more health risks over a given geographical area. The health risk prevalence level data to generate for each health risk a prevalence level forecast as a function of time and location. A health risk prevalence level map is generated for the given geographical area illustrating current and future health risk prevalence levels for each health risk at a plurality of locations within the given geographical area. Personal health status data are obtained for a given individual along with a proposed travel itinerary f covering at least a portion of the geographical area over a given time duration. The personal health status data, travel itinerary and map generate a personal health risk vulnerability model containing a quantification of vulnerability to the one or more health risks resulting from the travel itinerary.

Abstract: Individual health vulnerability is assessed by obtaining health risk prevalence level data containing health risk prevalence levels for one or more health risks over a given geographical area. The health risk prevalence level data to generate for each health risk a prevalence level forecast as a function of time and location. A health risk prevalence level map is generated for the given geographical area illustrating current and future health risk prevalence levels for each health risk at a plurality of locations within the given geographical area. Personal health status data are obtained for a given individual along with a proposed travel itinerary f covering at least a portion of the geographical area over a given time duration. The personal health status data, travel itinerary and map generate a personal health risk vulnerability model containing a quantification of vulnerability to the one or more health risks resulting from the travel itinerary.

Abstract: Individual health vulnerability is assessed by obtaining health risk prevalence level data containing health risk prevalence levels for one or more health risks over a given geographical area. The health risk prevalence level data to generate for each health risk a prevalence level forecast as a function of time and location. A health risk prevalence level map is generated for the given geographical area illustrating current and future health risk prevalence levels for each health risk at a plurality of locations within the given geographical area. Personal health status data are obtained for a given individual along with a proposed travel itinerary f covering at least a portion of the geographical area over a given time duration. The personal health status data, travel itinerary and map generate a personal health risk vulnerability model containing a quantification of vulnerability to the one or more health risks resulting from the travel itinerary.

Abstract: Individual health vulnerability is assessed by obtaining health risk prevalence level data containing health risk prevalence levels for one or more health risks over a given geographical area. The health risk prevalence level data to generate for each health risk a prevalence level forecast as a function of time and location. A health risk prevalence level map is generated for the given geographical area illustrating current and future health risk prevalence levels for each health risk at a plurality of locations within the given geographical area. Personal health status data are obtained for a given individual along with a proposed travel itinerary f covering at least a portion of the geographical area over a given time duration. The personal health status data, travel itinerary and map generate a personal health risk vulnerability model containing a quantification of vulnerability to the one or more health risks resulting from the travel itinerary.

Abstract: A microfluidic system-on-a-chip includes signal processing, light generation and detection, and fluid handling functions formed on a single substrate. The disclosed integrated system has a smaller footprint than device structures where individual components are manufactured separately and then assembled. Moreover, the integrated system obviates alignment challenges associated with conventionally packaged architecture.

Abstract: A method and system for tracking eye recovery. The method includes: receiving eye data from an eyewear, wherein the eye data includes images acquired by an image capturing device in the eyewear; analyzing the eye data to determine an eye recovery status, wherein the analyzing is carried out by comparing the eye data to an initial set of eye conditions; and providing a notification of the eye recovery status.

Abstract: A method and system for tracking eye recovery. The method includes: receiving eye data from an eyewear, wherein the eye data includes images acquired by an image capturing device in the eyewear; analyzing the eye data to determine an eye recovery status, wherein the analyzing is carried out by comparing the eye data to an initial set of eye conditions; and providing a notification of the eye recovery status.

Abstract: Disclosed is a computer-implemented method for recommending booths-to-visit to a user. The method includes: accessing a map database of an exhibition; accessing a history database that stores a plurality of records indicative of booths visited by a previous visitor in the exhibition; accessing a user record that includes data indicative of the booths which the user has visited; determining similarity level between each record in the history database and the user record, selecting one of the records according to the determined similarity level so as for the selected record to function as a reference record, usable to determine the booths not yet visited by the user; obtaining the user's current location in the exhibition, and determining a target booth, from the booths not yet visited by the user, by referring to the current location and the map database; and sending to the user a message indicative of the target booth.

Abstract: A device, method, and optical machine-readable code for providing context-aware guidance for healthcare. The device includes: a wireless communication module configured to receive contextual information pertaining to a user's health from one or more devices; an optical code reader to read an optical code encoded with a plurality of healthcare guidance messages; a processor to decode the optical code encoded with the plurality of healthcare guidance messages into a selected context-aware healthcare guidance message using the received contextual information pertaining to the user's health; and a screen for providing the selected context-aware healthcare guidance message to the user. The optical machine-readable code including: an optical code storing a plurality of healthcare guidance messages and encoded in a manner that one of the plurality of healthcare guidance messages can be selected using a decode key, wherein the optical code is provided on a container for medication.

Abstract: A method, system, and computer program product for adapting a corrective lens prescription. The system includes: an image capturing device; a lighting control device; a display; a memory; and a processor communicatively coupled to the image capturing device, the light generating device, the display, and the memory, wherein the processor is configured to perform the steps of a method including: generating a digital simulation for optometry, wherein the simulation is generated based on at least one lighting parameter, at least one activity parameter, at least one duration parameter, at least one personal parameter, and at least one target vision condition; recording user pupillary movement during the simulation; analyzing the recorded pupillary movement to determine a visual comfort assessment; and generating a message of the visual comfort assessment.

Abstract: A method and system for tracking eye recovery. The method includes: receiving eye data from an eyewear, wherein the eye data includes images acquired by an image capturing device in the eyewear; analyzing the eye data to determine an eye recovery status, wherein the analyzing is carried out by comparing the eye data to an initial set of eye conditions; and providing a notification of the eye recovery status.

Abstract: A method for non-intrusive event-driven prediction of a metric in a data processing environment is provided in the illustrative embodiments. At least one set of events is observed in the data processing environment, the set of events being generated by several processes executing in the data processing environment. A subset of the set of events are tracked for an observation period, the tracking resulting in bookkeeping information about the subset of events. A pattern of events is detected in the bookkeeping information. The pattern is formed as a tuple representing a process in the several processes, the metric corresponding to the process. A prediction model is selected for the tuple. The prediction model is supplied with the tuple and executed to generate a predicted value of the metric.

Abstract: An architecture, system, and method are provided for a commitment loop solution for on demand business activity management. A Business Performance Management (BPM) loop includes nodes each relating to different tasks for processing performance management trigger data generated by a business entity in real time. The beginning or sensing element of the loop is triggered by a token (representing a business event from a business process). The ending or executing element provides a business action to be implemented by the business entity. The token is passed from node to node sequentially in a particular order around the loop. Processing of the data represented by the token is governed by policies, which can be changed dynamically and follow a life cycle.

Abstract: An architecture, system, and method are provided for a commitment loop solution for on demand business activity management. A Business Performance Management (BPM) loop is defined as a set of nodes each relating to different tasks for processing performance management trigger data generated by a business entity in real time, where the beginning or sensing element of the loop is triggered by a token (representing a business event from a business process) and the ending or executing element provides a business action to be implemented by the business entity. The token is passed from node to node sequentially in a particular order around the loop. Processing of the data represented by the token is governed by policies, which can be changed dynamically and follow a life cycle. On demand business activity management of larger aggregates of business entities (i.e.

Abstract: A method, system, and computer usable program product for non-intrusive event-driven prediction of a metric in a data processing environment are provided in the illustrative embodiments. At least one set of events is observed in the data processing environment, the set of events being generated by several processes executing in the data processing environment. A subset of the set of events are tracked for an observation period, the tracking resulting in bookkeeping information about the subset of events. A pattern of events is detected in the bookkeeping information. The pattern is formed as a tuple representing a process in the several processes, the metric corresponding to the process. A prediction model is selected for the tuple. The prediction model is supplied with the tuple and executed to generate a predicted value of the metric.

Abstract: A method for non-intrusive event-driven prediction of a metric in a data processing environment is provided in the illustrative embodiments. At least one set of events is observed in the data processing environment, the set of events being generated by several processes executing in the data processing environment. A subset of the set of events are tracked for an observation period, the tracking resulting in bookkeeping information about the subset of events. A pattern of events is detected in the bookkeeping information. The pattern is formed as a tuple representing a process in the several processes, the metric corresponding to the process. A prediction model is selected for the tuple. The prediction model is supplied with the tuple and executed to generate a predicted value of the metric.

Abstract: A method (and apparatus) for adapting an input parameter, for dynamically invoking target Web services, and for adapting output results, includes receiving an invocation request including an input parameter in a first format. A semantic information representation module MetaWSDL (Meta Web Service Description Language), wherein the MetaWSDL includes a universal XML (eXtended Markup Language) representation which includes semantic information of a Web service method signature, is retrieved from a memory. A MetaWSDL processor is invoked to adapt the input parameter to a second format using the retrieved MetaWSDL. The target Web services are dynamically invoked, using the adapted parameter in the second format, and the output result in the first format is adapted to the second format, using the MetaWSDL.