Abstract: Systems and methods for ticket classification and response include labeling tickets with a ticket classifier that assigns a ticket label and an associated confidence score to each ticket. Tickets are clustered according to semantic similarity to form ticket clusters. A template associated with each ticket cluster is determined. Templates and the respective ticket clusters are clustered according to semantic similarity to form one or more ticket super-clusters. Tickets that have below-threshold confidence scores are labeled according to the one or more ticket super-clusters. The tickets are automatically responded to.

Abstract: Methods and systems for ticket classification and response include clustering tickets according to semantic similarity to form ticket clusters. A template associated with each ticket cluster is determined that includes an invariant portion and a variable portion. A new ticket sub-class, based on the variable portion of the template, is determined that represents a specific sub-type of an existing class. A ticket taxonomy is updated to include the new ticket sub-class. The tickets are labeled according to the updated ticket taxonomy. The tickets are automatically responded to.

Abstract: A method or apparatus for monitoring a system by detecting misbehaving components in the system is presented. A computing device receives historical data points based on a set of monitored signals of a system. The system has components that are monitored through the set of monitored signals. For each monitored component, the computing device performs unsupervised machine learning based on the historical data points to identify expected states and state transitions for the component. The computing device identifies one or more steady components based on the identified states of the monitored components. The computing device also receives real-time data points based on monitoring the set of signals from the system. For each identified steady component, the computing device examines the received real-time data points for deviation from the expected state and state transitions of the steady component. The computing device reports anomaly in the system based on the detected deviations.

Abstract: A charger circuit converts a supply voltage of a DC power supply to a charging voltage and a charging current to charge a battery. A battery charging method includes: setting the supply voltage to an initial voltage; checking whether the charging voltage reaches a predetermined voltage level; when the charging voltage is lower than the predetermined voltage level, delivering a control signal to the DC power supply to reduce the supply voltage; when the charging voltage or the charging current is reduced accordingly, delivering the control signal to the DC power to increase the supply voltage; and repetitively reducing and/or increasing the supply voltage until the charging voltage reaches the predetermined voltage level.

Abstract: A method for automatically detecting and diagnosing problems in computer system functioning includes determining changed objects from computer system monitoring data, calculating temporal correlations from errors and changes sequences for each changed object, identifying and ranking suspicious computer system behavior patterns from the temporal correlations, and outputting said ranked suspicious computer system behavior patterns.

Abstract: Techniques for log-based diagnosis for declarative-deployed applications can comprise: based on a labeled deployment declaration, classifying, by a device operatively coupled to a processor, a runtime log associated with an application, resulting in a classified runtime log. Techniques can also comprise: based on the classified runtime log, training, by the device, an aggregation model to represent a defined state of the application.

Abstract: A method or apparatus for monitoring a system by detecting misbehaving components in the system is presented. A computing device receives historical data points based on a set of monitored signals of a system. The system has components that are monitored through the set of monitored signals. For each monitored component, the computing device performs unsupervised machine learning based on the historical data points to identify expected states and state transitions for the component. The computing device identifies one or more steady components based on the identified states of the monitored components. The computing device also receives real-time data points based on monitoring the set of signals from the system. For each identified steady component, the computing device examines the received real-time data points for deviation from the expected state and state transitions of the steady component. The computing device reports anomaly in the system based on the detected deviations.

Abstract: A light emitting device current regulator circuit is disclosed. A light emitting device circuit has a first end for receiving light emitting device operation power, and a second end. The light emitting device current regulator circuit includes: an internal voltage generation circuit coupled to the second end, for generating an internal voltage according to a second end voltage to supply electrical power to the light emitting device current regulator circuit, wherein the supply voltage generation circuit includes a charge storage device for storing charges from the second end voltage to generate the supply voltage; and a current control circuit coupled to the second end, the current control circuit regulating the light emitting device current according to a control signal, wherein the control signal at least intermittently reduces the light emitting device current to zero or low current in order to raise the second end voltage.

Abstract: A computer-implemented method obtains an action sequence that includes a plurality of actions executed on behalf of a plurality of users for achieving at least one goal. An event sequence that includes a plurality of events associated with types of the plurality of actions is generated from the obtained action sequence. An association model based on the generated event sequence is determined. The association model defines a chronological relationship among events associated with the at least one goal.

Abstract: An adaptive buck converter of a charging cable includes: a power receiving interface for receiving a DC voltage and a cable current from a cable; a terminal communication interface for transmitting a charging voltage and a charging current to a connection terminal of the charging cable and for receiving a communication signal generated by a mobile device from the connection terminal; a power converting circuit for receiving the DC voltage and the cable current from the power receiving interface and for generating the charging voltage and the charging current; a monitor circuit arranged to operably detect the DC voltage or the cable current; and a data processing circuit configured for controlling the power converting circuit according to the communication signal. The data processing circuit further communicates with the mobile device through the terminal communication interface and the connection terminal in response to a detection result of the monitor circuit.

Abstract: The scope of the system changes to be considered for analysis for finding problematic changes is reduced in order to allow focusing on highly potential suspicious drifts caused by change sequences. The method and system includes a data cleaning module to remove irrelevant changes, a feature extraction and normalization module to extract the features of change objects, data annotation module to remove irrelevant changes based on patterns, and a clustering module to obtain groups for further analysis. Data cleaning is simplified using domain independent rules. Additional sources of change sequences are removed by application of pattern based techniques so as to narrow down problematic system changes to analyze for root cause analysis. Change error sequence and degree of temporal correlation to correlate system changes with errors, as well as change behavior patterns may be used for downsizing the diagnosis scope.

Abstract: Determining a characteristic of a configuration file that is used to discover configuration files in a target machine, a computer identifies, using information associated with a configuration item of a machine, a candidate configuration file related to the configuration item of the machine, from among a plurality of files from the machine. The computer extracts a value of a feature of the candidate configuration file and aggregates the candidate configuration file with a second candidate configuration file related to the same configuration item identified from among a plurality of files from a second machine, based on the extracted value. The computer then determines a configuration file related to the configuration item from among the aggregated candidate configuration files based on a result of the aggregation, and determines a characteristic of the configuration file related to the configuration item.

Abstract: A computer-implemented method obtains an action sequence that includes a plurality of actions executed on behalf of a plurality of users for achieving at least one goal. An event sequence that includes a plurality of events associated with types of the plurality of actions is generated from the obtained action sequence. An association model based on the generated event sequence is determined. The association model defines a chronological relationship among events associated with the at least one goal.

Abstract: Embodiments include methods and devices for migrating virtual assets over networks that have a first manager connected to a physical host a virtual machine run. Aspects include registering the physical host to a second manager in the network, creating the mapping relationship of the physical host between a database of the first manager and a database of the second manager and importing instance data and status data of the virtual machine of the physical host from the database of the first manager into the database of the second manager. Aspects also include switching the management for the physical host from the first manager to the second manager.

Abstract: Embodiments include methods and devices for migrating virtual assets over networks that have a first manager connected to a physical host a virtual machine run. Aspects include registering the physical host to a second manager in the network, creating the mapping relationship of the physical host between a database of the first manager and a database of the second manager and importing instance data and status data of the virtual machine of the physical host from the database of the first manager into the database of the second manager. Aspects also include switching the management for the physical host from the first manager to the second manager.

Abstract: Embodiments include method, computer program products and apparatuses for normalizing non-numeric features of files and corresponding apparatus. Aspects include segmenting at least one pair of positive instances of a non-numeric feature of a file into a number of tokens and comparing the tokens in the at least one pair of positive instances to obtain matching tokens. Aspects also include calculating weights of their matching the file, for the matching tokens, and storing the tokens and their weights in a token base.

Abstract: Embodiments include method, computer program products and apparatuses for normalizing non-numeric features of files and corresponding apparatus. Aspects include segmenting at least one pair of positive instances of a non-numeric feature of a file into a number of tokens and -comparing the tokens in the at least one pair of positive instances to obtain matching tokens. Aspects also include calculating weights of their matching the file, for the matching tokens, and storing the tokens and their weights in a token base.

Abstract: An adaptive buck converter of a charging cable includes: a power receiving interface for receiving a DC voltage and a cable current from a cable; a terminal communication interface for transmitting a charging voltage and a charging current to a connection terminal of the charging cable and for receiving a communication signal generated by a mobile device from the connection terminal; a power converting circuit for receiving the DC voltage and the cable current from the power receiving interface and for generating the charging voltage and the charging current; a monitor circuit arranged to operably detect the DC voltage or the cable current; and a data processing circuit configured for controlling the power converting circuit according to the communication signal. The data processing circuit further communicates with the mobile device through the terminal communication interface and the connection terminal in response to a detection result of the monitor circuit.

Abstract: Determining a characteristic of a configuration file that is used to discover configuration files in a target machine, a computer identifies, using information associated with a configuration item of a machine, a candidate configuration file related to the configuration item of the machine, from among a plurality of files from the machine. The computer extracts a value of a feature of the candidate configuration file and aggregates the candidate configuration file with a second candidate configuration file related to the same configuration item identified from among a plurality of files from a second machine, based on the extracted value. The computer then determines a configuration file related to the configuration item from among the aggregated candidate configuration files based on a result of the aggregation, and determines a characteristic of the configuration file related to the configuration item.

Abstract: A method, system, and computer program product, include extracting information related to one or more processes of one or more applications running on a virtual machine from a memory of the virtual machine, building at least one first application signature based on the extracted information, and identifying the one or more applications running on the virtual machine by matching the at least one first application signature with one or more second application signatures previously stored.