Abstract: A semiconductor chip includes a physical layer and a processing circuit. The physical layer includes an input/output circuit, at least one sequence checking circuit and at least one signal transmission path, wherein the at least one sequence checking circuit is configured to generate at least one test result signal according to a clock signal transmitted through the input/output circuit and at least one test data signal transmitted through the at least one signal transmission path. The processing circuit is electrically coupled to the physical layer and is configured to determine an operation status of the at least one signal transmission path according to a voltage level of the at least one test result signal.

Abstract: Interconnect structure packages (e.g., through silicon vias (TSV) packages, through interlayer via (TIV) packages) may be pre-manufactured as opposed to forming TIVs directly on a carrier substrate during a manufacturing process for a semiconductor die package at backend packaging facility. The interconnect structure packages may be placed onto a carrier substrate during manufacturing of a semiconductor device package, and a semiconductor die package may be placed on the carrier substrate adjacent to the interconnect structure packages. A molding compound layer may be formed around and in between the interconnect structure packages and the semiconductor die package.

Abstract: An interface device and a signal transceiving method thereof are provided. The interface device includes a slave circuit and a master circuit. The slave circuit is coupled to the master circuit and includes a first programmable delay line, a first output clock generator, and a first phase detector. The first programmable delay line provides a first adjusting delay amount according to a first adjust signal, and generates a first delayed clock signal by delaying a first clock signal according to the first adjusting delay amount. The first output clock generator generates a second clock signal according to the first delayed clock signal. The first phase detector detects a phase difference of the first clock signal and the second clock signal to generate first phase lead or lag information. The first adjust signal is generated according to the first phase lead or lag information.

Abstract: Embodiments of the present invention are directed to physiological sensing in a wearable device. Aspects include generating a multi-faceted feedback for a user. Generating the multi-faceted feedback includes generating a baseline physiological sampling schedule for a user, generating a quality-aware feedback of the user, generating a user-state-aware feedback of the user, and generating a context-aware feedback of the user. Aspects also include generating an adjusted physiological sampling schedule for the user based at least in part upon the multi-faceted feedback.

Abstract: An aspect includes querying, by a processor, a plurality of model data from a distributed data source based at least in part on one or more user characteristics. A plurality of sensor data is gathered associated with a condition of a user. A policy is generated including an end goal and one or more sub-goals based at least in part on the model data and the sensor data. The policy is iteratively adapted based at least in part on one or more detected changes in the sensor data collected over a period of time to adjust at least one of the one or more sub-goals. The policy and the one or more sub-goals are provided to the user.

Abstract: A model-driven and QoS-aware infrastructure facilitates the scalable composition of Web services in highly dynamic environments. An exception management framework supports two modes of exception management for business processes, providing a novel policy-driven approach to exception management implemented in the system infrastructure. Exception management is implemented in the system infrastructure, with exception handling policies supplied by individual business processes. Using the exception management framework, developers define exception policies in a declarative manner. Before a business process is executed, the service composition middleware integrates the exception policies with normal business logic to generate a complete process schema. This policy driven-approach can significantly reduce the development time of business processes through its separation of the development of the business logic and the exception handling policies.

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 several of the records according to the determined similarity level; determining the booths in the all of the selected records not yet visited by the user; adding the determined booths to a candidate list; and presenting at least one of the booths in the candidate list to the user.

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 several of the records according to the determined similarity level; identifying shared ones of the selected records; determining the booths in the shared ones of the selected records not yet visited by the user; adding the determined booths to a candidate list; and presenting at least one of the booths in the candidate list to the user.

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: 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: 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 several of the records according to the determined similarity level; determining the booths in the all of the selected records not yet visited by the user; adding the determined booths to a candidate list; and presenting at least one of the booths in the candidate list to the user.

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 several of the records according to the determined similarity level; identifying shared ones of the selected records; determining the booths in the shared ones of the selected records not yet visited by the user; adding the determined booths to a candidate list; and presenting at least one of the booths in the candidate list to the user.

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: Embodiments of the present invention are directed to physiological sensing in a wearable device. Aspects include generating a multi-faceted feedback for a user. Generating the multi-faceted feedback includes generating a baseline physiological sampling schedule for a user, generating a quality-aware feedback of the user, generating a user-state-aware feedback of the user, and generating a context-aware feedback of the user. Aspects also include generating an adjusted physiological sampling schedule for the user based at least in part upon the multi-faceted feedback.

Abstract: Embodiments of the present invention are directed to a computer-implemented method for stress monitoring. Methods include receiving, by a processor, a plurality of user stress labels corresponding to a plurality of events. Methods also include determining an individualized stress profile based at least in part upon the user stress labels. Methods also include receiving heart rate sensor data from a wearable device. Methods also include extracting a cardiovascular feature from the heart rate sensor data. Methods also include determining a stress index based at least in part upon the individualized stress profile and the cardiovascular feature. Methods also include outputting the stress index.

Abstract: Embodiments of the present invention are directed to a computer-implemented method for stress monitoring. Methods include receiving, by a processor, a plurality of user stress labels corresponding to a plurality of events. Methods also include determining an individualized stress profile based at least in part upon the user stress labels. Methods also include receiving heart rate sensor data from a wearable device. Methods also include extracting a cardiovascular feature from the heart rate sensor data. Methods also include determining a stress index based at least in part upon the individualized stress profile and the cardiovascular feature. Methods also include outputting the stress index.

Abstract: An aspect includes querying, by a processor, a plurality of model data from a distributed data source based at least in part on one or more user characteristics. A plurality of sensor data is gathered associated with a condition of a user. A policy is generated including an end goal and one or more sub-goals based at least in part on the model data and the sensor data. The policy is iteratively adapted based at least in part on one or more detected changes in the sensor data collected over a period of time to adjust at least one of the one or more sub-goals. The policy and the one or more sub-goals are provided to the user.

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: Embodiments of technical solutions are described for a health management system that facilitates adaptively tracking a health-management goal. An example computer-implemented method includes receiving a cumulative target for a predetermined duration for a user, and generating a target activity time-series for the predetermined duration to meet the cumulative target. The method further includes receiving a user-activity time-series for the user that indicates activity performed by the user to meet the cumulative target and monitoring a difference between the target activity time-series and the user-activity time-series. The method further includes determining, based on the difference, a probability value that indicates the user missing the cumulative target, and generating an updated target activity time-series for the user in response to the probability value being above a predetermined threshold.