Abstract: According to examples, an apparatus may include a processor and a memory on which are stored machine-readable instructions that when executed by the processor, may cause the processor to receive information regarding a performance characteristic of an application during predetermined time periods. The processor may calculate a rate of change in the performance characteristic over the predetermined time periods. Based on a determination that the performance characteristic of the application has changed over the predetermined time periods, the processor may tune values of a set of parameters for the application based on the calculated rate of change in the performance characteristic.

Abstract: A technique includes accessing data representing a state of a given investigation of a potential security threat to a computer system by a security analyst. The state includes a result of a current investigative step of the investigation, and the analyst conducting the investigation uses an investigation graphical user interface (GUI). The technique includes applying machine learning that is trained on observed investigations to determine a recommendation to guide the analyst in a next investigative step for the given investigation. The technique includes communicating the recommendation through an output provided to the investigation GUI.

Abstract: An apparatus may include a processor that may be caused to access handholding information indicating a hand configuration in which the apparatus is being held and compare the handholding information with previously-stored handholding authentication data of an authentic user. The previously-stored handholding authentication data may represent an authentic configuration in which the apparatus is held by the authentic user. The processor may be caused to determine that the apparatus is being held by the authentic user based on the comparison and cause an authenticated action to occur based on the determination.

Abstract: A device context of a device is identified based on device data including a current state of the device, and a user context of a user is identified based on user data including user interaction with a user interface for the device. A current task that the user is performing on the device in relation to the current state of the device is identified, and knowledge regarding how the user is performing the current task using the user interface is extracted, using an artificial intelligence technique with respect to the identified device context and the identified user context. The extracted knowledge for the identified current task is stored within a knowledge base, and can be used to adapt the user interface for another user performing the current task on the device in the current state.

Abstract: Context information associated with a network node is determined. For example, a network node (e.g., a server) has indicated that an application on the network node appears to be having performance problems. The context information may be to gather data about the application, such as, load, number of users accessing the application, etc. The context information comprises one or more rules for dynamically configuring how data is collected on the node. For example, the rule may indicate what specific statistics should be gathered from the application in real-time. The context information is sent to the network node. Node information is received from the network node in response to sending the context information. The node information is collected based on the one or more rules for configuring how data is collected on the network node. The node information is organized and is displayed on a user interface to a user.

Abstract: A request to load an application into memory for execution is received. The application is stored in one or more blocks in a blockchain. The application is validated by running a blockchain hash. In response to validating the application by running the blockchain hash, the application is loaded from the blockchain into a memory. The application is then executed in the memory. This provides a secure method of loading an application into memory.

Abstract: A request to add a new block to a blockchain is received. Data associated with the new block is scanned to identify malware and/or an anomaly. In response to identifying the malware and/or the anomaly in the data associated with the new block, an action is taken. The action includes: rejecting the request to add the new block to the blockchain, or removing the malware/anomaly from the new block and adding the new block to the blockchain. In a second embodiment, a malware event is identified that identifies malware/an anomaly in a block in a blockchain. In response to the malware event, an action is taken. The action includes: consolidating the blockchain, bypassing the block in the blockchain, consolidating the blockchain and bypassing the block in the blockchain, and deleting an encryption key that was used to encrypt the associated data that comprises the malware and/or the anomaly.

Abstract: An endpoint device monitors loading of dynamically loaded libraries (DLLs) by a process, such as during execution of the process on the endpoint device. The endpoint device can generate an endpoint-independent representation of the DLLs upon exiting of the process. The endpoint device may generate a hash, such as a one-way hash, of the endpoint-independent representation. The endpoint device may transmits the process and the hash to a management device to detect in a machine learning manner whether loading of the DLLs during the execution of the process was anomalous.

Abstract: According to examples, an apparatus may include a processor that may generate a migration assessment for resources of a computer system. In particular, the apparatus may logically divide topological information to facilitate identification of a resource, components used by the resource, and dependencies. The system further enables users to specify user-defined migration parameters that specify the migration. For instance, the parameters may specify a cost associated with the migration of the component, a license model of the component, a security requirement of the component, a performance of the component, a customization of the component, or requirement of the component. Migration assessments and decisions may be stored to train machine-learned models. For instance, the model may assess whether a parameter will be satisfied by using a certain cloud service and whether substitutes have sufficiently satisfied dependencies based on observed migration assessments and actual migrations.

Abstract: A primary blockchain for a software application is created that comprises a first block associated with a software component of the software application. An event is received that is associated with the software component. In response to receiving the event, a component blockchain is created that is associated with the software component. The component blockchain comprises a second block associated with the event. The component blockchain links to the primary blockchain. This provides a structure for managing supply chains of software components. As new software components are received, the new software components can be managed and tracked for quality/security.

Abstract: A copy of a blockchain is stored. The stored copy of the blockchain is copied from a blockchain in a distributed blockchain ledger. An event associated with the blockchain in the distributed ledger is identified. In response identifying the event associated with the blockchain in the distributed ledger, a compromise of the blockchain in the distributed ledger is identified, such as, identifying one or more blocks of the blockchain that have been compromised. In a second embodiment, a request to add a new block to a blockchain is identified. In response identifying the request to add the new block to the blockchain, a consensus vote to add the new block to the blockchain is monitored. A determination is made to determine if the consensus vote is below a threshold. In response to the consensus vote being below the threshold, an audit of the blockchain is completed.

Abstract: A first application name is received. For example, the first application name may be the name of the application, a Universal Resource Locator (URL) of a web site (e.g., microfocus.com), and/or the like. The first application name represents a computer application of a plurality of computer applications that the user logs into. A global password is received from the user. The global password is used as a seed to login to the plurality of computer applications. The global password is converted to a phonetic password. A first application password is generated for the first computer application based on the first application name and the phonetic password. For example, the first application name and the phonetic password are used as an input to a key deviation function. The first application password is then displayed to the user. The user can then use the displayed password to login to the first application.

Abstract: Embodiments of the disclosure provide systems and methods for performing functional testing on an application using artificial intelligence. According to one embodiment, a method for performing functional testing on an Application Under Test (AUT) can comprise maintaining, by a processor of an edge computing device, a model comprising a script defining one or more automatic tests for the AUT and trained data identifying objects of a user interface of the AUT. One or more tests can be executed on the AUT by the processor of the edge computing device based on the script and the trained data identifying the objects of the user interface of the AUT. Results of executing the one or more tests on the AUT can be provided by the processor of the edge computing device.

Abstract: Example embodiments relate to generating sets of recommended inputs for changing predicted results of a predictive model. The examples disclosed herein access, from a database, a historical set of inputs and results of a predictive model. A function is approximated based on the historical set of inputs and results, and a gradient of the function is computed using a result of the function with respect to a local maximum value of the function. A set of recommended inputs is generated based on the gradient of the function, where a recommended input produces a positive result of the function.

Abstract: According to examples, an apparatus may include a processor that may schedule virtual users in a load test of a system under test. An apparatus may access user-specified or prestored points. Each point may represent a number of virtual users that is to place a load on the system under test at a given time. The apparatus may apply curve fitting, such as Bézier curve fitting, to generate a curve on a graph in which one axis represents time and another axis represents a number of virtual users at a given time. In some examples, the apparatus may use a smoothing factor to adjust the contours of the generated curve. The apparatus may use the curve to determine curve-fitted points to schedule virtual users at times corresponding to the curve-fitted points. To instantiate the virtual users, the system may use coroutines to reduce computational requirements for instantiating the virtual users.

Abstract: In some examples, test relevancy prediction for code changes may include ascertaining files for a commit for a build, and for each test of a plurality of tests, determining a score based on a weight assigned to a file of the ascertained files. Test relevancy prediction for code changes may further include ordering each test of the plurality of tests according to the determined score, and identifying, based on the ordering of each test of the plurality of tests, tests from the plurality of tests for which the score exceeds a specified threshold. The identified tests may represent tests that are to be applied to the build.

Abstract: Examples include a multi-node cluster having a node with a clustering layer. The clustering layer may be located between an application programming interface (API) layer and a service layer and the multi-node cluster may be associated with a database. In some examples, the clustering layer may discover whether a number of nodes associated with the multi-node cluster has changed. Based, at least in part, on the determination that the number of nodes associated with the multi-node cluster has changed, at the clustering layer, the database may be sharded and a new API call may be issued to the API layer.

Abstract: A non-transitory computer readable medium can store machine readable instructions that when accessed and executed by a processing resource cause a computing device to perform operations. The operations can include establishing a connection between data stores (such as a relational data store and a graph engine), wherein the connection includes a shared memory buffer storing data in a data format according to internal structures of the graph engine. The connection between the data stores is bi-directional. The connection enables data that is stored in the shared memory to be processed by either of the graph engine and the relational database. Upon receiving a query, the graph engine or the relational database can be selected to process the data based on a query. The data can be processed by the selected one of the graph engine or the relational database.

Abstract: Format-preserving encryption and decryption processes are provided. The encryption and decryption processes may use a block cipher. A string that is to be encrypted or decrypted may be converted to a unique binary value. The block cipher may operate on the binary value. If the output of the block cipher that is produced is not representative of a string that is in the same format as the original string, the block cipher may be applied again. The block cipher may be repeatedly applied in this way during format-preserving encryption operations and during format-preserving decryption operations until a format-compliant output is produced. Selective access may be provided to portions of a string that have been encrypted using format-preserving encryption.

Abstract: In one example, a system for gesture buttons can include a processing resource and a non-transitory computer readable medium having stored thereon instructions to cause the processing resource to perform a first function in response to a gesture provided to the user interface, display a gesture button on the user interface, and perform a second function that is different than the first function in response to the gesture provided on the gesture button displayed on the user interface.