Abstract: Managing a brake system of a vehicle includes collecting sensor data from one or more sensors in or around the vehicle, calculating brake effectiveness values based on the sensor data, calibrating the brake effectiveness values based on environmental context data associated with the vehicle, accumulating the calibrated brake effectiveness values as a dataset, generating a prediction curve or formula based the dataset, and scheduling a maintenance alarm for the brake system based on the brake effectiveness values.

Abstract: Method and systems for tagging code include organizing code units in a hierarchical structure that associates the code units with respective description units. The hierarchical structure is filtered to remove un-meaningful description units from the hierarchical structure. Feature tags are generated based on a weighted sum of term frequencies in the description units of the hierarchical structure to associate features with respective code units.

Abstract: Technical solutions to automate alert remediation are described. One aspect includes a method that includes receiving a plurality of alerts from an application monitoring system, the plurality of alerts associated with a plurality of remediation procedures respectively. The method also includes selecting a subset of alerts from the plurality of alerts. The method also includes identifying a subset of remediation procedures corresponding to the subset of alerts and analyzing compliance, with a service level agreement, of an execution of the entire subset of remediation procedures. In response to the execution of the entire subset of remediation procedures being non-compliant, the latest alert that was added, is removed from the subset of alerts, and a remediation procedure corresponding to the latest alert is removed from the subset of remediation procedures. The method includes executing the entire subset of remediation procedures.

Abstract: Technical solutions to automate alert remediation are described. One aspect includes a method that includes receiving a plurality of alerts from an application monitoring system, the plurality of alerts associated with a plurality of remediation procedures respectively. The method also includes selecting a subset of alerts from the plurality of alerts. The method also includes identifying a subset of remediation procedures corresponding to the subset of alerts and analyzing compliance, with a service level agreement, of an execution of the entire subset of remediation procedures. In response to the execution of the entire subset of remediation procedures being non-compliant, the latest alert that was added, is removed from the subset of alerts, and a remediation procedure corresponding to the latest alert is removed from the subset of remediation procedures. The method includes executing the entire subset of remediation procedures.

Abstract: A cognitive conversation system that generates effective diagnostic questions is provided. The cognitive conversation system receives a set of currently known symptoms (or currently available answers to diagnostic questions) of a reported problem or fault. The system identifies (i) a set of possible root causes of the reported problem based on the currently known symptoms and (ii) probabilities for the set of possible root causes by using a bipartite graph data structure that links possible symptoms with possible root causes. Upon determining that at least one possible root cause has a probability that is higher than a threshold, the system presents an explanation or solution associated with the at least one possible root cause. Upon determining that none of the possible root causes in the set of possible root causes has a probability higher than the threshold, the system presents a question based on information entropy that is computed based on probabilities of the identified possible root causes.

Abstract: This disclosure provides a method for road condition prediction. The method comprises receiving, for at least one source vehicle, sensor data collected by a sensor associated with the source vehicle. The method further comprises identifying, based on the sensor data, at least a location with an abnormal road condition and a responsive action taken by the source vehicle. The method further comprises notifying, the location with the abnormal road condition and the responsive action to at least one target vehicle that is expected to pass the location. This disclosure also provides a computer system and a computer software product for road condition prediction.

Abstract: A method, a computer program product, and a computer system for managing a device in a vehicle. The computer system obtains a first configuration of a first device of a first vehicle, wherein the first configuration is set by a user of the first vehicle and the first configuration meets a driving habit of the user. The computer system generates an ergonomic model for the user based on the first configuration. The computer system determines a second configuration of a second device of a second vehicle based on the ergonomic model.

Abstract: A method, computer system, and a computer program product for structured assessments is provided. The present invention may include initializing a structured assessment. The present invention may also include receiving a plurality of signup data from a user based on the initialized structured assessment. The present invention may then include collecting a plurality of user data from an IoT device based on the received plurality of signup data. The present invention may further include processing the collected plurality of user data. The present invention may also include generating a feedback report based on the processed plurality of user data, and outputting the generated feedback report to the user.

Abstract: A method, computer system, and a computer program product for structured assessments is provided. The present invention may include initializing a structured assessment. The present invention may also include receiving a plurality of signup data from a user based on the initialized structured assessment. The present invention may then include collecting a plurality of user data from an IoT device based on the received plurality of signup data. The present invention may further include processing the collected plurality of user data. The present invention may also include generating a feedback report based on the processed plurality of user data, and outputting the generated feedback report to the user.

Abstract: Integration of personalized traffic information is provided. Contextual information for a client device is collected. Based, at least in part, on the contextual information, traffic information is received. The traffic information is received from one or more broadcast stations to which the traffic information has been mapped based, at least in part, on classification of the traffic information. A communications device is caused to communicate the traffic information.

Abstract: Managing a brake system of a vehicle includes collecting sensor data from one or more sensors in or around the vehicle, calculating brake effectiveness values based on the sensor data, calibrating the brake effectiveness values based on environmental context data associated with the vehicle, accumulating the calibrated brake effectiveness values as a dataset, generating a prediction curve or formula based the dataset, and scheduling a maintenance alarm for the brake system based on the brake effectiveness values.

Abstract: A method for managing a vehicle brake system includes collecting sensor data from one or more sensors in or around the vehicle, calculating brake effectiveness values based on the sensor data, calibrating the brake effectiveness values based on environmental context data associated with the vehicle, accumulating the calibrated brake effectiveness values as a dataset, generating a prediction curve or formula based the dataset, and scheduling a maintenance alarm for the brake system based on the brake effectiveness values.

Abstract: An authentication method, system, and computer program product for decoding brain activity into a brain pattern sequence, applying brain dialogue to interact with a user while the user is entering the brain pattern sequence, and matching the brain pattern sequence with a predetermined password to allow access to a system.

Abstract: A system and method to build feature evolution models for existing applications (“apps”) in the market based on open app data repositories). A feature evolution model of an app depicts the app name, its historical versions (historical version labels, release timestamps of each version), rating values of each version, and structured features (e.g., umbrella features and low-level features) each version introduces, improves or deletes. There is further extracted from the app description and release logs the app name, historical version labels, release timestamps, use the rating info of the app to extract and assign rating values for each version of the app, and apply NLP techniques and source code analysis techniques to extract “structured features” of the app through analyzing the app description, the release logs, and corresponding source code revisions of the app. Upon the built feature evolution models, various feature insights may be easily extracted and generated.

Abstract: Aspects of the present invention generally relate to a system, method, and computer program product for transforming APIs from legacy system-of-record to new system-of-engagement using complete API process logic. API candidates are generated from execution traces for transforming the API from the legacy system to a new system of record. An embodiment of the invention is first, add implementers into a legacy systems and collect the instrumenter output as execution traces. Second, rank the execution traces and find the most frequent execution traces. Third, consolidate the execution traces by merging common trace segments. The codes covered by the consolidated execution traces will be considered as candidate API components. Fourth, expand the merged execution traces and find related execution traces and check the connectivity between different nodes.

Abstract: A system and method to build feature evolution models for existing applications (“apps”) in the market based on open app data repositories). A feature evolution model of an app depicts the app name, its historical versions (historical version labels, release timestamps of each version), rating values of each version, and structured features (e.g., umbrella features and low-level features) each version introduces, improves or deletes. There is further extracted from the app description and release logs the app name, historical version labels, release timestamps, use the rating info of the app to extract and assign rating values for each version of the app, and apply NLP techniques and source code analysis techniques to extract “structured features” of the app through analyzing the app description, the release logs, and corresponding source code revisions of the app. Upon the built feature evolution models, various feature insights may be easily extracted and generated.

Abstract: A mechanism is provided for optimizing web page pre-loading. Source codes of a web page file are analyzed to generate a module dependency graph that describes load dependency relationships between respective modules in the web page file. A load flow graph that indicates load flows of respective modules in the web page file is generated based on the module dependency graph. The load flow graph and the module dependency graph are analyzed to determine pre-load sequences of respective modules in the web page file. A pre-load profile is then generated based on the determined pre-load sequences.

Abstract: Method and systems for tagging code include organizing code units in a hierarchical structure that associates the code units with respective description units. The hierarchical structure is filtered to remove un-meaningful description units from the hierarchical structure. Feature tags are generated based on a weighted sum of term frequencies in the description units of the hierarchical structure to associate features with respective code units.

Abstract: Method and systems for tagging code include building a code structure based description unit association model (CDAM) that associates code units with respective description units and organizes the code units in a hierarchical structure. The CDAM is filtered to remove un-meaningful description units through cross-validation between code units. Feature tags are generated based on a weighted sum of term frequencies in the description units of the CDAM to associate features with respective code units.

Abstract: A system and method to build feature evolution models for existing applications (“apps”) in the market based on open app data repositories). A feature evolution model of an app depicts the app name, its historical versions (historical version labels, release timestamps of each version), rating values of each version, and structured features (e.g., umbrella features and low-level features) each version introduces, improves or deletes. There is further extracted from the app description and release logs the app name, historical version labels, release timestamps, use the rating info of the app to extract and assign rating values for each version of the app, and apply NLP techniques and source code analysis techniques to extract “structured features” of the app through analyzing the app description, the release logs, and corresponding source code revisions of the app. Upon the built feature evolution models, various feature insights may be easily extracted and generated.