A signature matching hardware accelerator system comprising one or more hardware accelerator circuits, wherein each of the hardware accelerator circuit utilizes a compressed deterministic finite automata (DFA) comprising a state table representing a database of digital signatures defined by a plurality of states and a plurality of characters, wherein the plurality of states are divided into groups, each group comprising a leader state having a plurality of leader state transitions and one or more member states, each having a plurality of member state transitions is disclosed. The hardware accelerator circuit comprises a memory circuit configured to store the leader state transitions within each group of the compressed DFA, only the member state transitions that are different from the leader state transitions for a respective character within each group of the compressed DFA and a plurality of member transition bitmasks associated respectively with the plurality of member state transitions.

A computer-implemented method for decomposing a decision table includes decomposing, by a computer processor, a decision table into a first sub-table and a second sub-table. The decision table includes two or more columns, and the decomposition is based on a semantic model describing relations among the two or more columns of the decision table. The first sub-table and the second sub-table together represent the decision table.

An apparatus in one embodiment comprises an ingestion manager, a plurality of ingestion engines associated with the ingestion manager, and an analytics platform configured to receive data from the ingestion engines under the control of the ingestion manager. The ingestion manager is configured to interact with one or more of the ingestion engines in conjunction with providing data to a given one of a plurality of analytics workspaces of the analytics platform. For example, the analytics workspaces of the analytics platform are illustratively configured to receive data from respective potentially disjoint subsets of the ingestion engines under the control of the ingestion manager. Additionally or alternatively, the ingestion manager may be configured to implement data-as-a-service functionality for one or more of the analytics workspaces of the analytics platform.

Disclosed are a head mounted display (HMD), and a method for controlling the same. The HMD includes: a body having a display unit; a lens driving unit provided at the body, and configured to move a lens unit spaced apart from the display unit, wherein the lens driving unit includes: a lens frame having a first tube portion protruded in a first direction, and coupled to the body; a lens housing having a second tube portion protruded in the first direction and having the lens unit, the second tube portion relatively moved on the first tube portion; a link unit coupled to the lens frame and the lens housing, and configured to move the lens housing; and a driving unit provided at one side of the first tube portion, and configured to operate the link unit.

A signature matching hardware accelerator system comprising one or more hardware accelerator circuits, wherein each of the hardware accelerator circuit utilizes a compressed deterministic finite automata (DFA) comprising a state table representing a database of digital signatures defined by a plurality of states and a plurality of characters, wherein the plurality of states are divided into groups, each group comprising a leader state having a plurality of leader state transitions and one or more member states, each having a plurality of member state transitions is disclosed. The hardware accelerator circuit comprises a memory circuit configured to store a single occurrence of a most repeated leader state transition within each group, the unique leader state transitions comprising the leader state transitions that are different from the most repeated leader state transition within the respective group; and leader transition bitmasks associated respectively with the leader states within each group.

Vulnerability testing tasks can be received and distributed, via a work scheduler, to computer test environments. Each of the test environments can have a detector computing component running in the environment. Each detector component can respond to receiving one of the tasks from the work scheduler by conducting a vulnerability test on an endpoint of a target, detecting results of the vulnerability test, generating output indicating the results of the vulnerability test, and sending the output to an output processor. The work scheduler can initiate dynamic scaling of the test environments by activating and deactivating test environments in response to determining that the test environments are overloaded or underloaded, respectively. Also an overall time-based limit on testing for a target can be enforced via the work scheduler.

Managing databases implemented in a cloud computing environment. A method includes detecting that a database implemented in the cloud computing environment is in a state of non-use. The method further includes as a result of detecting that a database implemented in the cloud computing environment is in a state of non-use, instantiating a workload in the cloud computing environment to deactivate the database. The workload is configured to store metadata for the database and database data remotely in cloud storage such that the database can be reactivated at a later time.

User interface information related to relevant events of interest is provided. Events can occur anywhere in a document, and may or may not be relevant to a user utilizing an assistive technology (AT) application, such as a screen reader. A provider-side signaling system component determines whether raised events are relevant to the user. In some examples, when an application makes a plurality of attribute changes in a document at once, the signaling provider batches the related events as a single transaction, and generates a generalized annotation describing the changes. The signaling provider further packages the event notification, and sends the event notification to a client-side signaling system component. The signaling client receives the notification, and determines whether to alert the user of the event(s) based on verbosity settings. The AT application is enabled to interpret the event notification and alert the user in a meaningful way.

To identify objects shared by entities and to, in turn, identify free space in nonvolatile storage, a computer system uses a probabilistic data structure which tests whether an element is a member of a set. Such probabilistic data structures are created for entities in the storage system that share objects. The probabilistic data structure for an entity represents the objects that are used by that entity. When an entity is deleted, each object used by that entity is compared to the probabilistic data structures of other entities to determine if there is a likelihood that the object is used by one or more of the other entities. If the likelihood determined for an object is above an acceptable threshold, then the object is not deleted. If the likelihood determined for an object is below the set threshold, then the object can be deleted and the corresponding storage locations can be marked as free.

Technologies for a distributed Internet of Things (IoT) system are disclosed. Several IoT devices may form a peer-to-peer network without requiring a central server. Information may be stored in a distributed manner in the distributed IoT system, allowing for storing information without transmitting it to a remote server, which may be costly and introduce security or privacy risks. Each IoT device of the distributed IoT system includes a machine learning algorithm that is capable of uncovering patterns in the input of the distributed IoT system, such as a pattern of user inputs in certain situations, and the distributed IoT system may adaptively anticipate a user's intentions.