Abstract: Examples herein involve authorization of use of cryptographic keys based on cryptocurrency payments. An example method includes analyzing a request for a cryptographic key of a key server where the request may be received from a requesting device and the cryptographic key is used to decrypt or encrypt a message of the request, and authorizing use of the cryptographic key to decrypt or encrypt the message based on a balance in a cryptocurrency wallet associated with the request.

Abstract: According to an example, a model is selected from models including an augmented buyer model based on probabilities of conceivable transitions, and each conceivable transition includes a multi-step transition between a first URL and a second URL via at least one intermediate URL of the website. A user is determined to likely be a buyer or a non-buyer based on interaction data and the selected model. The user is presented with an offer that encourages the user to buy from the website upon the determination that the user is a buyer.

Abstract: A sub-archive is initiated. The sub-archive saves changes that have occurred since a previous final archive. Changes to an operational database (i.e., the operational database that is being archived) are allowed during the first sub-archive. A final archive is initiated in series after the sub-archive has completed. The final archive does not allow changes to the operational database when the final archive is active. In one embodiment, the sub-archive may comprise a plurality of sub-archives that depend on an amount of outstanding changes that exist in the operational database.

Abstract: This document describes techniques and apparatuses for enhancing data-analytic visualizations of a data analytics system. A computing device captures input data and output data associated with a data-analytic visualization generated by an advanced user using the data analytics system. The input data and output data are mapped together for defining the data-analytic visualization generated. A machine learning model is trained relative to the mapped input data and output data for generating the data-analytic visualization. During a normal usage of the data analytics system by a user, the trained model generates data-analytic visualizations to suggest to the user responsive to input data from the user. An optional threshold is set and applied relative to the data-analytic visualizations generated. If a data-analytic visualization meets the threshold, the data analytics system reports the data-analytic visualization.

Abstract: In some examples, a system processes event data and video frames produced by a program during execution of the program, the event data representing user actions with respect to a graphical user interface (GUI) of the program. The system identifies an area of the GUI that corresponds to a respective user action of the user actions, wherein identifying the area of the GUI uses a first video frame before an event corresponding to the respective user action, and a second video frame after the event corresponding to the respective user action. The system identifies, based on the identified area, a test object representing a user interface (UI) element, and generates a test script for testing the program, the test script including the test object.

Abstract: A technique includes receiving, by a computer, user input representing creation of a first programmatic description of a first test object of source code to be tested. The technique includes, in response to receiving the user input, determining, by the computer, based on other programmatic descriptions of other test objects, a recommendation of a parameter to be used in the first programmatic description to identify the first test object. The technique includes causing, by the computer, a display of the recommendation.

Abstract: Applications under test (AUT) may be tested by automated testing systems utilizing machine vision to recognize visual elements presented by the AUT and apply inputs to graphical elements, just as a human would. By utilizing the smallest image patch available, processing demands of the testing system are minimized. However, the image patch used to identify a portion of an AUT must be identifiable to the automated system. By selecting image patches that comprise the smallest size, but can be identified in an AUT by an automated system using machine vision, even as the AUT display is resized, reproportioned, noisy, or otherwise altered from the testing platform that was utilized for training.

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 method includes: identifying, by a runtime instrumentation agent of a web server, a plurality of attack surfaces of a web application executed on the web server; generating, by the runtime instrumentation agent, a plurality of hash values, where each hash value is generated based on one of the plurality of attack surfaces; and transmitting, by the runtime instrumentation agent, the plurality of hash values to an attack server external to the web server, where the attack server is to determine whether to scan each attack surface based on the plurality of hash values.

Abstract: A computing device includes a processor and a machine-readable storage storing instructions. The instructions are executable by the processor to: receive an input string including sensitive data to be encrypted; identify a first portion and a second portion of the input string, the first portion comprising the sensitive data; select, from a plurality of hash functions, a hash function based on the second portion; and generate a hash value of the first portion using the selected hash function.

Abstract: A first sub-blockchain and a second sub-blockchain are retrieved. The sub-blockchains are predefined fragments of a blockchain that are intended to be used to build a larger blockchain. At least one of the first sub-blockchain and the second sub-blockchain was originally stored in a read-only computer memory or was originally stored in a barcode/RFID tag. A blockchain that comprises the first sub-blockchain and the second sub-blockchain is created. The blockchain is used to track inventory of a product.

Abstract: A network traffic flow is directed to a computing services system is determined as being unrelated to the service that the system provides. In response, the network traffic flow is deflected away from the computing services system. Demand of the sender of the network traffic flow is managed by shaping the deflected network traffic flow in responding to the sender and/or by misinforming the sender in responding to the sender.

Abstract: A database having multiple data columns is disclosed. Whether metadata for the multiple data columns resides in multiple different metadata columns may be determined. If so, the metadata may be consolidated into a single consolidated metadata column.

Abstract: Testing software applications often requires a balancing of thoroughness versus the time and computing resources available to perform such tests. By performing a static analysis on candidate software source code and, from the static analysis, configuring a dynamic analysis component to execute the tests, allows for extraneous tests to be omitted. For example, performing certain vulnerability attacks on a function may be futile if the attack requires a string input but the function only accepts integers. By combining static and dynamic analysis, unnecessary tests may be omitted and the results of each analysis process correlated to identify actual vulnerabilities or falsely indicted vulnerabilities reported by one of the static or dynamic analysis component.

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 track traffic events and user interface (UI) events in parallel and record a first event of a hybrid test script. The first event may be recorded in a traffic mode to record one or more of the traffic events. The processor may transition from the traffic mode to a UI mode and, responsive to the transition from the traffic mode to the UI mode, may share session data between the traffic events and the UI events. Based on the session data, the processor may record a second event of the hybrid test script in the UI mode.

Abstract: An event is detected. The event is a designated event for capturing a snapshot of a blockchain where the blockchain is not replicated in a distributed ledger. For example, the event may be where a specific number of new blocks have been added to the blockchain. In response to detecting the event, a capstone block or a lock block is created. The capstone block or the lock block is added to the blockchain. A copy of the capstone block or the copy of the lock block sent to a trusted authority. The copy of the capstone block or the copy of the lock block is used to validate some or all the blockchain to detect if the blockchain has been changed. For example, validating the blockchain may be to detect if the blockchain has been tampered with or has become corrupted.

Abstract: A nonce is sent to a user communication device. Obfuscated biometric data is received from the user communication device. The obfuscated biometric data is generated by an obfuscation process that uses the nonce and first biometric data. The first biometric data is generated from a biometric of a user at the user communication device. For example, the first biometric data may be biometric data from a fingerprint scan. The obfuscation process is reversed by using the first nonce to produce the first biometric data. The first biometric data is compared to known biometric data of the user. In response to the first biometric data being within a threshold of the known biometric data, the user is authenticated.

Abstract: A security rule associated with an application is identified. This may be done continuously and verified using machine learning models to ensure that the environment characterized by the data has not changed. For example, a security rule may be which ports are open/closed on a firewall. In response to identifying the security rule associated with the application, a security test based on the security rule is generated. For example, the security test may be to test all the ports on the firewall to see which ports are open/closed. The security test against the application is executed to determine if the security rule has been implemented properly by the application.

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 provide for automated testing of an Application Under Test (AUT) that utilizes an infinite scroll element. Automated testing of the AUT can comprise executing one or more test scripts on the AUT which can present a user interface including a plurality of elements and the test scripts can perform one or more functional tests on the AUT through the plurality of elements of the user interface. During performance of the one or more functional tests, a scroll event associated with an element of the user interface can be detected and a determination can be made as to whether the element associated with the scroll event is an infinite scroll element. In response to determining the element is an infinite scroll element, a set of test results for the functional test can be recorded including results for the infinite scroll element and an interaction with the infinite scroll element.