A dynamic vision sensor (DVS) or change detection sensor reacts to changes in light intensity and in this way monitors how a scene changes. This disclosure covers both single pixel and array architectures. The DVS may contain one pixel or 2-dimensional or 1-dimensional array of pixels. The change of intensities registered by pixels are compared, and pixel addresses where the change is positive or negative are recorded and processed. Analyzing frames based on just three values for pixels, increase, decrease or unchanged, the proposed DVS can process visual information much faster than traditional computer vision systems, which correlate multi-bit color or gray level pixel values between successive frames.

The present disclosure is directed to a scalable, extensible, fault-tolerant system for stateful joining of two or more streams that are not fully synchronized, event ordering is not guaranteed, and certain events arrive a bit late. The system can ensure to combine the events or link the data in near real-time with low latency to mitigate impacts on downstream applications, such as ML models for determining suspicious behavior. Apart from combining events, the system can ensure to propagate the needed entities to other product streams or help in entity resolution. If any of the needed data is yet to arrive, a user can configure a few parameters to achieve desired eventual and attribute consistency. The architecture is designed to be agnostic of stream processing framework and can work well with both streaming and batch paths.

Provided are a method and apparatus for generating a topological graph, an anomaly detection method and apparatus, a device and a storage medium. The method for generating a topological graph includes acquiring a preset event stream, where the preset event stream corresponds to a normal log execution path; determining a dependent event pair in the preset event stream; determining a range of a transfer interval corresponding to the dependent event pair, where a transfer interval represents the time difference between adjacent occurrences of two events in the dependent event pair; and generating an event topological graph according to the range of the transfer interval and the transfer probability corresponding to the dependent event pair, where the transfer probability represents the conditional probability between the two events in the dependent event pair.

In some examples, a system comprises a network interface; a storage device comprising machine-readable instructions; and a processor coupled to the network interface, the processor to access the storage device, wherein execution of the machine-readable instructions causes the processor to: collect crash event data; categorize the crash event data by an application executing when the crash event occurred; identify a crash event corresponding to the crash event data; create an identifier for the crash event; compare the identifier of the crash event to a list of work items, wherein each work item has an identifier; and update the list of work items based on the comparison.

An instrumentation analysis system processes data streams by executing instructions specified using a data stream language program. The data stream language allows users to specify a search condition using a find block for identifying the set of data streams processed by the data stream language program. The set of identified data streams may change dynamically. The data stream language allows users to group data streams into sets of data streams based on distinct values of one or more metadata attributes associated with the input data streams. The data stream language allows users to specify a threshold block for determining whether data values of input data streams are outside boundaries specified using low/high thresholds. The elements of the set of data streams input to the threshold block can dynamically change. The low/high threshold values can be specified as data streams and can dynamically change.

A network device receives a data packet including a source address and a destination address. The network device drops the data packet before it reaches the destination address and generates an error message indicating that the data packet has been dropped. The network device encapsulates the error message with a segment routing header comprising a list of segments. The first segment of the list of segments in the segment routing header identifies a remote server, and at least one additional segment is an instruction for handling the error message. The network device sends the encapsulated error message to the remote server based on the first segment of the segment routing header.

Techniques for in-band modification of event notification preferences for server events are provided. One method comprises obtaining an event notification; providing the event notification to a target device based on rule-based preferences of a user associated with the target device;

obtaining a reply to the event notification from the target device, wherein the reply comprises event preferences of the user; and updating the rule-based preferences of the user based on the event preferences of the user. The updating of the rule-based preferences of the user may comprise creating, modifying and/or canceling at least one event preference rule of the user. A plurality of the event preference rules matching the event notification may be resolved in an order determined by one or more event preference rule resolution criteria.

It is possible to reduce analysis cost of a management system.

The management system includes a CPU and manages one or more storage devices that provide, to a higher-level device, one or more volumes for inputting and outputting data. The CPU is configured to collect performance information of the volume from the storage device at a predetermined first time interval and detect a QoS violation of the performance information of the volume at a second time interval longer than the first time interval.

An example system includes a processor to receive mouse dynamics data of a session to be analyzed and a uniform resource locator (URL) category mapping. The processor can group the mouse dynamics data into a plurality of groups using the URL category mapping. The processor can separately extract features from each of the plurality of groups to generate a plurality of groups of features for the session. The processor can input the groups of features into a trained classification model. The processor can receive an output score from the trained classification model.

A tiling display apparatus includes a plurality of display modules connected to one another through a first interface circuit and a second interface circuit and a set board receiving a defect occurrence and position signal, generated in a broken-down module of the plurality of display modules, from the broken module through the first interface circuit in a first period, generating a defect recognition completion signal in a second period succeeding the first period, and transferring the defect recognition completion signal to the broken-down module through the second interface circuit in the second period.