Examples herein involve estimating a first position of a mobile device based on first communication signals, assigning a first set of particles to a number of respective first sampling locations within a threshold distance of the first position, adjusting the assignment of the first set of particles to second sampling locations based on movement of the mobile device, and estimating a second position of the mobile device based on the second sampling locations.
G01S 5/02 - Position-fixing by co-ordinating two or more direction or position-line determinationsPosition-fixing by co-ordinating two or more distance determinations using radio waves
H04W 4/029 - Location-based management or tracking services
Examples herein involve estimating a first position of a mobile device based on first communication signals, assigning a first set of particles to a number of respective first sampling locations within a threshold distance of the first position, adjusting the assignment of the first set of particles to second sampling locations based on movement of the mobile device, and estimating a second position of the mobile device based on the second sampling locations.
G01S 5/02 - Position-fixing by co-ordinating two or more direction or position-line determinationsPosition-fixing by co-ordinating two or more distance determinations using radio waves
H04W 4/029 - Location-based management or tracking services
3.
RECORDING TEST USAGE OF GRAPHICAL USER INTERFACE WITH BLOCKED INPUTS
An apparatus may include a processor (102) that may generate automated test scripts to test graphical user interface (GUI) functions of an application under test (AUT) (235). The apparatus may generate a screen element (220) that is overlaid onto at least one or all of the GUIs (210). The screen element (220) may therefore receive user inputs directed at the GUI (210), but block such inputs from being provided to the GUI (210). The user inputs received at the screen element (220) may be recorded in an automated test script for later replay. Blocking the user input may prevent a change in appearance of a GUI element (212) that would otherwise result from the user input, facilitating automated location of the GUI element (212) during replay of the automated test script.
A technique includes processing, by a computer, data representing a software defect report to extract features from the software defect report. The software defect report contains information that identifies a defect in a software product. The technique includes applying, by the computer, a feedforward neural network classifier to the features to identify a developer to assign to the identified defect.
A monitoring utility program into a software container in which a containerized virtual machine application is running. The monitoring utility program is to monitor the containerized virtual machine application running within the software container. Monitoring information regarding the containerized virtual machine application is periodically pulled from the monitoring utility program.
According to examples, an apparatus may include a processor that may internationalize an automated test script that was generated to test a Graphical User Interface (GUI) in the first human language. When the GUI is internationalized into a second human language, the automated test script may no longer function. As such, the system may employ computer vision techniques to analyze the GUI in the first human language and the GUI in the second human language to identify text and GUI elements that correlate with one another. Based on the correlation, the system may internationalize the automated test script to function on the GUI in the second human language.
An apparatus (100) may include a processor (102) that may identify and execute workflows based on simulated network addresses such as simulated uniform resource locations ("URLs" ). The system (200) may generate recorded automation scripts that automatically complete some or all of the tasks of a workflow. The system (200) may store the automation scripts in association with the workflow and a simulated URL. The simulated URL may include a string that does not literally resolve to a document on a networked resource. Rather, the simulated URL may instead identify and indicate that a corresponding workflow is to be executed. A browser extension (214) of a browser (212) may intercept URLs that are provided to a browser (212), determine that a simulated URL has been entered, and provide the simulated URL to a replay engine (302) that identifies and executes the automated script associated with the simulated URL.
In some examples, a system executes a program that generates a user interface (UI) screen, provides a user input event to the program during execution, and captures images of the UI screen before and after the user input event. The system determines, based on the captured images, whether a first region of the UI screen changed in response to the user input event, and indicates the first region as a user interactive region and adds an element representing the user interactive region to a representation of user interactive regions of a UI of the program.
G06F 3/00 - Input arrangements for transferring data to be processed into a form capable of being handled by the computerOutput arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
9.
TEST SCRIPT GENERATION BASED ON EVENT DATA AND VIDEO FRAMES
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.
A computing device includes a processor and a medium storing instructions. The instructions are executable by the processor to: identify, based on a blob detection analysis, a plurality of potential input elements in a graphical user interface (GUI); determine a set of rows including potential input elements that are in a horizontal alignment and in a same size range; determine a set of columns including potential input elements that are in a vertical alignment and in a same size range; determine a set of input elements comprising multiple potential input elements that are located at intersections of the identified set of rows and the identified set of columns; and perform automated testing of the GUI using the determined set of input elements.
A technique includes displaying, by a computer using a graphical interface, a map of a geographical area, where the map includes political boundaries. The technique includes displaying, by the computer, graphical images on the map representing a plurality of aspects that are associated with the management of a plurality of projects as corresponding geographical features on the map. The technique includes graphically segregating, by the computer, the plurality of projects on the map using the political boundaries; receiving input, via interaction with the displayed map; and changing, by the computer, in response to the interaction, how a given aspect of the plurality of aspects of a given project of the plurality of projects is represented on the map.
In some examples, with respect to asymmetric-man-in-the-middle capture based application sharing protocol traffic recordation, a dynamic-link library that alters application programming interface calls with respect to communication between an application sharing protocol client and an application sharing protocol server may be injected into the application sharing protocol client. Based on the injected dynamic-link library, data from the communication between the application sharing protocol client and the application sharing protocol server may be ascertained. Further, based on the ascertained data, a test script may be generated to test operation of an application associated with the communication between the application sharing protocol client and the application sharing protocol server.
A computing device includes a processor and a machine-readable storage medium storing instructions. The instructions are executable by the processor to: generate a graphical user interface including a first portion and a second portion, the first portion including a tree menu comprising a plurality of topic links; and in response to a user selection of a first topic link in the tree menu, present a first topic window in the second portion of the graphical user interface. The instructions are further executable by the processor to, in response to a user command to minimize the first content window: minimize the first content window; and present a snapshot window in the second portion of the graphical user interface, the snapshot window comprising a first snapshot of the first topic window.
In some examples, with respect to intent and context-aware dialogue based virtual assistance, an intent of an inquiry may be determined using an intent classification model. A determination may be made as to whether the determined intent matches a pre-specified intent of a plurality of pre-specified intents. Based on a determination that the determined intent does not match the pre-specified intent, a question related to the inquiry may be generated. Another intent of the inquiry may be determined by analyzing a response to the question using the intent classification model. A determination may be made as to whether the determined other intent matches another pre-specified intent of the plurality of pre-specified intents. Based on a determination that the determined other intent does not match the other pre-specified intent, a deep learning model may be utilized to predict a response to the inquiry.
A computing device includes a processor and a machine-readable storage medium storing instructions. The instructions are executable by the processor to: receive input data defining an information technology (IT) change request; in response to a receipt of the input data, perform a first risk classification of the IT change request using a decision tree model; generate a graphical user interface based on the first risk classification, the graphical user interface indicating risk impacts for each of a plurality of request features, and the graphical user interface including a graphic representation of the decision tree model; in response to a user modification to a first request feature of the plurality of request features in the graphical user interface, automatically perform a second request analysis using the decision tree model; and automatically update the graphical user interface based on the second request analysis.
In some examples, a system represents tasks of a project as feature nodes of a force-directed graph, and connects, in the force-directed graph, sub-feature nodes representing sub-features associated by links to the feature nodes in the force-directed graph. The system sets a size of each respective sub-feature node of the sub-feature nodes based on an amount of resource usage expended on a respective sub-feature represented by the respective sub-feature node. The system causes display of the force-directed graph, and collapses or expands a portion of the force-directed graph responsive to user interaction with the force-directed graph.
A system receives a source database language statement according to a first dialect, determines a pattern of the source database language statement, the pattern comprising an abstract representation of the source database language statement, and checks whether the determined pattern is present in a cache of translations between patterns according to the first dialect and corresponding patterns according to a second dialect different from the first dialect. In response to the determined pattern being present in the cache of translations, the system converts, using a corresponding translation in the cache of translations, the source database language statement according to the first dialect to a respective target database language statement according to the second dialect.
A computing device includes a processor and a medium storing instructions. The instructions are executable by the processor to: in response to a receipt of an electronic request comprising one or more structured data fields and one or more unstructured data fields, identify a set of previous electronic requests using the one or more structured data fields of the received electronic request; train a probabilistic classification model using at least one structured data field of the identified set of previous electronic requests; execute the trained probabilistic classification model using the one or more unstructured data fields of the received electronic request; and automatically select a request handler using an output of the executed probabilistic classification model.
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.
Content items and a dynamic menu element are displayed. Responsive to selection and dragging of the dynamic menu element over a particular content item, a menu of actions contextual to the particular content item is displayed, and the particular content item is highlighted to indicate that the displayed menu is related to the particular content item. Responsive to release of the dynamic menu element over the particular content item, the displayed menu is rendered actionable so that the actions thereof selectable; prior to release of the dynamic menu element over the particular content item, the displayed menu is non-actionable and no action is selectable. Responsive to selection of a specific action of the menu being displayed, the selected specific action is performed in relation to the particular content item.
G06F 3/0488 - Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
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.
In one example, a system for topic image flows can include a processing resource and a non-transitory computer readable medium having stored thereon instructions to cause the processing resource to generate an image flow of a plurality of topic headers when a user scroll speed of a document including text exceeds a threshold scroll speed, receive a selection of a topic header from the plurality of topic headers, and display, on a user interface, a portion of the document that corresponds to the selected topic header from the plurality of topic headers.
In some examples, a system receives a user interface (UI) context of a UI, the UI context identifying a relationship between UI elements of the UI, and translates, based on the identified relationship between the UI elements in the UI context, text in the UI from a first language to a second language.
A computer implemented method includes: accessing from a configuration management database, by a virtualization manager, configuration data for a first computing node of a computing system; generating, by the virtualization manager, a set of attribute/value pairs for the first computing node using the configuration data; and managing, by the virtualization manager, a first container on the first computing node using the set of attribute/value pairs for the first computing node.
In some examples, an apparatus for automated removal of noise in a frequency domain receives an image captured by an image sensor, converts at least a portion of the image into a frequency domain image, identifies a position in the frequency domain image, the position indicating a boundary between target content in the frequency domain image and noise in the frequency domain image, and removes content in the frequency domain image outside the boundary, to produce a noise-attenuated image.
In one example in accordance with the present disclosure, a method may include receiving a digit sequence including a subset of N digits encoded with semantic information and determining a set of possible combinations for the N digits in the subset. The method may also include establishing a mapping between each possible combination in the set of possible combinations and a corresponding integer sequence belonging to a set of integer sequences. Each integer sequence in the set of integer sequences is of the length of N-1. The method may also include identifying a selected integer sequence corresponding to the subset and replacing n-1 digits from the subset with the selected integer sequence. The method may also include replacing a digit of the subset with a digit value calculated to produce a valid checksum for the entire first digit sequence, wherein the first digit is not included in the n-1 digits.
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.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
The present disclosure is to determine a probable root cause of a performance issue. For example, a method can include processing, using a processor, a set of calls for a Hypertext Transfer Protocol (HTTP) transaction wherein individual calls of the set of calls have a set of parameters; and identifying, using the processor, that the HTTP transaction has a performance issue that falls below a predetermined level of a performance metric. Further steps can include separating, using the processor, the set of calls into a first group with the performance issue and a second group without the performance issue; discovering, using the processor, a common subset of parameters among the first group; and determining that the common subset of parameters is a probable root cause of the performance issue if the common subset of parameters is not found in the second group.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
Examples include monitoring performance of an application. Some examples include tracing a set of transactions associated with an application, generating a transaction interface that includes an area to receive a selection of a transaction of the set of transactions, and receiving a selected transaction. Based on the selected transaction, a transaction monitor rule may be built to monitor the selected transaction. Based on the transaction monitor rule, a performance interface may be generated that includes an area having transaction performance information of the selected transaction.
In one example in accordance with the present disclosure, a method may include separating a list of keywords into a set of word tokens and a set of wildcard tokens. The method may also include removing each wildcard token in the set of wildcard tokens that is inferred by at least one word token in the set of word tokens and removing each wildcard token in the set of wildcard tokens that is inferred by at least one other wildcard token in the set of wildcard tokens. The method may also include executing a search query comprising a new list of keywords that includes each wildcard token not removed from the set of wildcard tokens.
In some examples, a system includes a segment identification engine and a coverage determination engine. The segment identification engine may a code segment in application code for updating a code coverage threshold applicable to the code segment. The coverage determination engine may update the code coverage threshold for the code segment based on a usage frequency of the code segment and a change frequency of the code segment.
G06F 11/36 - Prevention of errors by analysis, debugging or testing of software
G06F 9/06 - Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
Example implementations relate to comparable UI object identifications. Some implementations may include a data capture engine to capture data points during test executions of the application under test. The data points may include, for example, test action data and application action data. Additionally, some implementations may include a data correlation engine to correlate each of the data points with a particular test execution of the test executions, and each of the data points may be correlated based on a sequence of events that occurred during the particular test execution. Furthermore, some implementations may also automatically identify, based on the correlated data points, a set of comparable UI objects.
Examples herein disclose via use of a physical processor, detecting a specific application programming interface (API) call to interact with an application running on a production server. Based on the detection of the specific API call, the examples assist, using the physical processor, a scanning session based on the specific API call. Using the physical processor, the examples identify a modification to the application based on the scanning session.
Examples discussed herein disclose, among other things, an encrypting device. The encrypting device may include a key engine to obtain a first key associated with a first access level, and a second key associated with a second access level. The encrypting device may also include a multi-key encryption engine to encrypt a first portion of the plaintext with the first key, and encrypt a second portion of the plaintext with the second key, where the first portion may include more detailed information than the second portion, and where the first access level may be higher than the second access level.
G06F 21/72 - Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer to assure secure computing or processing of information in cryptographic circuits
Examples disclosed herein relate to data objects associated with private set intersection (PSI). Some examples disclosed herein may enable identifying a set of server elements and a set of data objects. Each data object of the set of data objects may be associated with at least one server element of the set of server elements. Some examples further enable sending the set of server elements and the set of data objects to a client computing device that has a set of client elements. A private set intersection (PSI) between the set of server elements and the set of client elements may be inaccessible by the client computing device, and a subset of the set of data objects that are associated with the PSI may be accessible by the client computing device.
Example implementations relate to creating a simulation environment. For example, a system for simulation environment creation may include a system controller. The system may also include a functional agent coupled to and executed by the system controller to create a simulation environment based on input comprising real user data and sensor data continuously received from a plurality of sources, perform tests within the simulation environment, continuously react to events occurring during the tests within the simulation environment, and continuously verify results of the tests.
In some examples, a method includes accessing a test script configured to test an application at a target screen resolution and testing the application using the test script when a current screen resolution of the test system is different from the target screen resolution. The testing may include initializing a system web browser to run the application and overwriting a size parameter of the system web browser to cause a content window of the system web browser to display application content at the target screen resolution instead of the current screen resolution.
In some examples, first segment of computer language text in a first rule in IT workflow data and a second segment of computer language text in a second rule in the IT workflow data may be identified. In some examples, a similarity score may be determined between the first and the second rules based on a comparison of the first segment with the second segment.
In some examples, a system includes a scan execution engine and a scan adaptation engine. The scan execution engine may execute a scan of a web application hosted on a web host. During scan execution, the scan adaptation engine may adapt a subsequent scan portion for later execution based on a scan metric received from a monitoring agent that monitors the web application, the web host, or both.
In some examples, a first difference may be determined across respective first and second workflow elements in a first hierarchical level of respective first and second IT workflow data. A second difference may be determined across respective third and fourth workflow elements in a second hierarchical level of the respective first and second IT workflow data. A display representing the first and second differences may be generated.
Structured data archival with reduced downtime is disclosed. One example is a system including a deployer that manages an active table (AT), and a non-active table (NAT), and creates an intermediate table (IT) to record, during data archival, changes to the data to be archived. The deployer creates triggers on the AT and the NAT to facilitate the record, by the IT, of the changes to the data to be archived. An archiver initiates the data archival by archiving the copy of the data to be archived from the NAT, merges the recorded data from the IT to the NAT upon receiving an indication that the client access to the AT is not enabled, and switches the client access from the AT to the NAT by changing a table synonym, where the client access to the NAT is enabled upon completion of the data archival.
An example method comprises performing for each class from a plurality of classes: constructing binary training set for the class, the binary training set including labeled cases for that class from the main training set other labeled cases from the main training set; training classifier for the class on the binary training set; computing a local calibration threshold using scores of the labeled cases in the binary training set; and adjusting all scores of the label cases in the binary training set with the local calibration threshold to meet a global decision threshold. The method also comprises determining, with the processor, a global hierarchical calibration threshold by using the adjusted scores for all classes to optimize a performance measurement of all trained classifiers. The method further comprises classifying, with the processor, a new case by using a previously trained classifier, a local calibration threshold, and the global hierarchical calibration threshold.
G06F 17/30 - Information retrieval; Database structures therefor
G06F 15/18 - in which a program is changed according to experience gained by the computer itself during a complete run; Learning machines (adaptive control systems G05B 13/00;artificial intelligence G06N)
G06F 17/00 - Digital computing or data processing equipment or methods, specially adapted for specific functions
Example implementations relate to simulating an environment. For example, a system for environment simulation may include a simulation engine to build an environment simulation to mimic portions of a real environment relevant to a detected anomaly trend, an acceleration engine to simulate, within the environment simulation, a scenario associated with the detected anomaly at a rate faster than the scenario occurs in the real environment, a abnormal behavior engine to detect a abnormal behavior associated with the scenario, and an adaptation engine to modify a device within the real environment to be adaptive to the scenario, based on the detected abnormal behavior.
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Examples disclosed herein relate to visualizations of computer program transactions. The examples enable obtaining a transaction record of a computer program. The transaction record may include a call stack of a plurality of procedure calls and a self-time of each procedure call. The examples enable generating a graphical representation of the transaction record having a plurality of two-dimensional shapes aligned with a first axis and a second axis. Each two-dimensional shape represents a procedure call, and a first dimension of each shape represents a call-time of each procedure call while a second dimension of each shape represents the self-time of each procedure call. The shapes are positioned in the graphical representation to reflect relative positions within the call stack.
Examples discussed herein disclose, among other things, a method. The method includes, among other things, obtaining a plaintext, obtaining a key from a plurality of keys, and determining whether the plaintext is longer than a predefined threshold length. If the plaintext is longer than the predefined threshold length, the method may encrypt the plaintext with the key to generate a first ciphertext having a length of the plaintext, where the character at a predefined position within the first ciphertext belongs to a first subset of characters. And if the plaintext is not longer than the predefined threshold length, the method may encrypt the plaintext with the key to generate a second ciphertext, which is longer than the plaintext, where the character at the same predefined position in the second ciphertext belongs to a second subset of characters.
H04L 9/14 - Arrangements for secret or secure communicationsNetwork security protocols using a plurality of keys or algorithms
H04L 9/06 - Arrangements for secret or secure communicationsNetwork security protocols the encryption apparatus using shift registers or memories for blockwise coding, e.g. D.E.S. systems
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.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06F 17/00 - Digital computing or data processing equipment or methods, specially adapted for specific functions
47.
STRENGTH OF ASSOCIATIONS AMONG DATA RECORDS IN A SECURITY INFORMATION SHARING PLATFORM
Examples disclosed herein relate to strength of associations among data records in a security information sharing platform. Some examples may enable creating, in the security information sharing platform, an association between a security indicator comprising an observable, and a data record. Some examples may further enable determining strength of the association between the security indicator and the data record based on at least one of: a likelihood of change in the association; a creator of the association; an aging rate of the association; or a quality of evidence that supports the association.
Examples disclosed herein relate to visualization of associations among data records in a security information sharing platform. Some examples may enable creating, in the security information sharing platform, an association between a first data record comprising a security indicator, and a second data record. Some examples may further enable providing a visual representation of the first data record, the second data record, and the association, wherein the first data record represents a first node in the visual representation, the second data record represents a second node in the visual representation, and the association represents an edge that connects the first node and the second node.
Examples disclosed herein relate to source entities of security indicators. Some examples disclosed herein enable identifying, in a security information sharing platform, a security indicator that is originated from a source entity where the security indicator comprises an observable. Some examples further enable determining a reliability level of the source entity based on at least one of: security events, sightings of the observable, a first set of user feedback information that is submitted for the security indicator by users of the security information sharing platform, or a second set of user feedback information that is collected from external resources that are external to the security information sharing platform.
Examples discussed herein disclose, among other things, an encrypting device. The encrypting device may include a format preserving encryptor to obtain a plaintext, obtain a key from a plurality of keys stored in a key database, and encrypt the plaintext using the key to produce a ciphertext having a length of the plaintext. The encrypting device may also include a key reference embedder to obtain a key reference associated with the key, and generate an extended ciphertext by adding to the ciphertext a set of characters associated with the key reference, such that the key reference can be determined based on the extended ciphertext.
A method for user interest and relationship determination may include distributing a first and a second set of pairs to a plurality of data nodes. The method may also include calculating, on a first data node, a probability of a user's interest in a product based on an observable factor and a latent factor and calculating, on a second data node, a probability of a likelihood of a relationship between the user and a second user, based on an observable factor and a latent factor. The method may also include determining a most likely interest and a most likely relationship of the user and predicting a potential interest of the user based on the most likely interest and the most likely relationship.
Example implementations relate to protecting data of a particular data type. For example, a system for protecting data of a particular type may include a configuration engine to receive a stream of data, where a portion of the stream of data includes data of a particular data type. The configuration engine may determine the particular data type to be protected based, at least in part, on a format associated with the stream of data. Further, the system for protecting data of a particular type may include a cryptography engine to protect the data of the particular data type, and a generation engine to output the stream of data such that at least the data of the particular data type is protected.
An example technique involves sending, from a user device associated with a particular user, a request for a confirmation message setting. The request may include an indication of an identity of the particular user. The example technique involves determining that an operation included in a predetermined set of operations has been cued. The example technique involves receiving a response comprising an indication of a confirmation message setting for the cued operation prior to performing the cued operation. The confirmation message setting may be based on data generated responsive to the sent request. The confirmation message setting for the cued operation may include an indication of whether or not to output a confirmation message. The example technique involves determining, based on the received response, whether to prompt the particular user via a user interface of the user device for a response to a confirmation message prior to performing the cued operation.
According to an example, an application code graph of an application may be received and the programming structures of the application may be ranked based on a ranking model. When the information regarding code changes associated with the application are received, the ranking model may be applied to the programming structures associated with the code changes. The impact of the code changes on the application performance may be determined and corresponding recommendations may be produced based on the determined impact.
In example implementations, a method executed by a processor is provided. The method collects historical sales data. The historical sales data is divided into a training data set and a testing data set. A classification model is calculated based on the training data set and validated with the testing data set. Information associated with a pending sale is received. The information comprises a plurality of factors. Based upon the classification model, a prediction is made whether the pending sale will successfully close. An action plan that includes changing at least one of the plurality of factors based on the predicting is generated.
G06Q 30/06 - Buying, selling or leasing transactions
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
56.
COMMUNITIES ON A SECURITY INFORMATION SHARING PLATFORM
Examples disclosed herein relate to generating communities on a security information sharing platform. Some examples may enable identifying a set of community attributes to be used to generate a community on the security information sharing platform that enables sharing of security information among a plurality of communities. Some examples may enable authorizing a first user to access community-based security information of the community where the first user is associated a set of user attributes that satisfy the set of community attributes. A security indicator may be obtained from the first user of the community. Information related to the security indicator may be obtained from a second user of the community. Some examples may enable including the security indicator and the information related to the security indicator in the community-based security information of the community.
A log event cluster analytics management method may involve storing a first portion of an entire cluster dictionary in a transient memory, storing at least a second portion of the entire cluster dictionary in a persistent database and comparing a new log event message to the first portion of the overall cluster dictionary. In response to not assigning the new log event message to any cluster in the first portion of the entire cluster dictionary in the transient memory, selecting a subset of clusters of the at least second portion of the cluster dictionary in the persistent database, comparing the new log event message to a cluster of the selected subset of clusters and assigning the new log event message to the cluster of the selected subset of clusters based upon the comparison.
A method, a non-transitory computer-readable storage medium comprising instructions to implement the method, and a corresponding system to determine whether a window is an error block are disclosed. Wherein a window in an image may be identified, then at least one property of the window may be identified. Based on the at least one property, whether the window is an error block may be determined.
A method of coordinating operation of a number of different modules in a computing system that includes: receiving, at a state machine of a module, publication of an occurrence of a number of triggering states at modules of the computing system to which a current state of the state machine is subscribed; in response to receiving publication of the occurrence the number of triggering states at modules of the computing system to which a current state of the state machine is subscribed, sending a request to a central supervisor for authorization to advance to a next state; and in response to receiving authorization from the central supervisor, advancing the state machine to the next state corresponding to the number of triggering states that have occurred.
A system and method for a text search of a database, including converting a text search expression to a query plan and implementing the text search as the query plan on the database. The implementing of the text search includes a one-pass indexing as a single scan of an inverse index table associated with the database.
A system and method for a text search of a database. A text search expression is converted to a query plan having multiple search tokens. A one-pass indexing of an inverted word index filters the inverted word index based on a search condition and identifies the applicable documents having the multiple search tokens.
Examples disclosed herein relate to encryption of community-based security information. Some examples may enable authorizing a user of a community to access an encrypted data item (e.g., at least an encrypted portion of community-based security information of that community) using a decryption key. The community may be generated on a security information sharing platform based on a set of community attributes. The decryption key may comprise a private key corresponding to each user attribute of a set of user attributes that are associated with the authorized user where the set of user attributes satisfy the set of community attributes.
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
A method includes, in a distributed environment for a test execution of an application, measuring impairment in the distributed environment; and, during the application test execution, subtracting the measured impairment from a test impairment that is artificially introduced into the application test execution based on a test profile.
An example device in accordance with an aspect of the present disclosure includes an interleaved connector including a plurality of layers of conducting material interspersed with insulating material. A plurality of electrodes are to identify a change in capacitance of the interleaved connector to indicate a penetration of the device.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
65.
Cryptographic system with halting key derivation function capabilities
A halting key derivation function is provided. A setup process scrambles a user-supplied password and a random string in a loop. When the loop is halted by user input, the setup process may generate verification information and a cryptographic key. The key may be used to encrypt data. During a subsequent password verification and key recovery process, the verification information is retrieved, a user-supplied trial password obtained, and both are used together to recover the key using a loop computation. During the loop, the verification process repeatedly tests the results produced by the looping scrambling function against the verification information. In case of match, the trial password is correct and a cryptographic key matching the key produced by the setup process may be generated and used for data decryption. As long as there is no match, the loop may continue indefinitely until interrupted exogenously, such as by user input.
H04L 9/06 - Arrangements for secret or secure communicationsNetwork security protocols the encryption apparatus using shift registers or memories for blockwise coding, e.g. D.E.S. systems
66.
POPULARITY-BASED PROMOTION OF APPLICATION FEATURES
According to an example, usage of a plurality of features of an application by users in each of a plurality of tenants may be accessed. Popularities of the plurality of features among the users in each of the plurality of tenants may be determined and a feature of the plurality of features to be promoted to the users in the first tenant may be identified based upon the determined popularities of the features. Additionally, a visual callout to the identified feature may be caused to be displayed to the users in the first tenant to promote the identified feature to the users in the first tenant.
Example implementations relate to determining visual testing coverages. Some implementations may include a principal application screen identification engine to identify, based on an image processing analysis of a set of screenshots of an application under test, principal application screens that represent a set of test executions of the application under test. Some implementations may also include a user interface element identification engine to identify user interface elements in the principal application screens. Some implementations may also include a visual testing coverage engine to automatically identify, based on an analysis of gaze data and the user interface elements, a visual testing coverage of the application under test.
G06F 3/033 - Pointing devices displaced or positioned by the userAccessories therefor
G06F 3/037 - Pointing devices displaced or positioned by the userAccessories therefor using the raster scan of a cathode-ray tube [CRT] for detecting the position of the member, e.g. light pens cooperating with CRT monitors
G06F 3/0487 - Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser
68.
DETERMINING A FUNCTIONAL STATE OF A SYSTEM UNDER TEST
Example implementations relate to determining a functional state of a system under test. For example, a system to determine a functional state of a system under test may include a system controller to execute a functional test of the system under test by invoking a subset of a plurality of functional agents to interact with the system under test. Further, the system may include an agent repository to interact with the system controller and store the plurality of functional agents. Also, the system may include a state module to determine a functional state for the system under test by querying each of the subset of functional agents and comparing aggregated results from the subset of functional agents against defined metrics for the system under test.
Examples disclosed herein relate, among other things, to a method. The method may obtain a time series comprising a plurality of data points associated with a sub-segment of a segment, obtaining a plurality of weights associated with a plurality of data point pairs from the plurality of data points, and based on the plurality of weights and the plurality of data point pairs, determine whether the time series comprises a trend. Based on a determination that the time series comprises a trend, the method may calculate a trend score for the trend based on at least one characteristic of at least one of the segment and the sub-segment, and provide the trend for display.
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06F 3/048 - Interaction techniques based on graphical user interfaces [GUI]
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Examples disclosed herein relate, among other things, to a first computing device including a server optimization module communicatively coupled to a server application. The server optimization module may receive, from a second computing device, a resource request identifying a resource, and send to the second computing device a packet list identifying a set of packets associated with the resource, where each packet describes differences between different versions of the resource. The server optimization module may also receive from the second computing device a set of packet requests, each packet request identifying a packet from the set of packets, and for each packet in the set of packets, send the packet to the second computing device based on a determination that the packet has been updated since the packet has been last sent to the second computing device.
Example implementations are described that include displaying a graphical element related to privacy risk information for an application. A processor of a computing device may determine, for the application, a privacy attention score based on first privacy risk information. The processor may determine, for the application, an aggregated privacy assessment score from a plurality of privacy risk scores that are based on second privacy risk information. The processor may cause a graphical element representing a combination of the privacy attention score and the aggregated privacy assessment score to be displayed via a display device.
Example implementations relate to test execution comparisons. Some implementations may include a data capture engine to capture data points during test executions of the application under test. The data points may include, for example, test action data and application action data. Additionally, some implementations may include a data correlation engine to correlate each of the data points with a particular test execution of the test executions, and each of the data points may be correlated based on a sequence of events that occurred during the particular test execution. Furthermore, some implementations may also automatically compare the test executions, based on the correlated data points, to identify commonalities.
Examples disclosed herein relate to software development managements. Some of the examples enable identifying a set of rules related to a software development methodology. Each rule of the set of rules may be associated with a rule-specific score. Some of the examples further enable monitoring a software development process, and determining, during the monitoring, whether at least one rule of the set of rules is invoked by an action performed by a user. If determined that a particular rule of the set of rules is invoked, a software development management score may be dynamically adjusted based on the rule-specific score associated with the particular rule. Some of the examples further enable providing a recommended action to increase the software development management score.
Example implementations relate to generating application flow entities. Some implementations may include accessing a series of image frames from a test execution of an application and comparing, using an application test analysis device comprising a digital image processor, the image frames to each other to identify a subset of the image frames. The subset of image frames may be identified, for example, based on actions occurring during the test execution. Some implementations may also include automatically generating, using the application testing system, an application flow entity that represents the test. The application flow entity may be generated based on the subset of image frames.
Product recommendations are based on selected subsets of user and product attributes. Stored coefficients of transformations of the user vectors composed of subsets of attributes of respective users browsing a website and stored coefficients of transformations for the product vectors composed of subsets of attributes of respective products are accessed. The maximum values of a set of inner products of the user vectors with each of the product vectors are selected. Products associated with the maximum values are recommended to the respective users.
In some examples, time-series datasets received from a system may be temporally aligned. In some examples, one of the time-series datasets may be deduplicated. In some examples, whether an anomaly has occurred in the system may be determined based on a non-deduplicated time-series dataset of the time-series datasets.
G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
Examples disclosed herein relate to prediction models for concurrency control types. Some of the examples enable generating a prediction model based on training data. The training data may comprise a set of access data associated with a data object. The set of access data may comprise: values for a set of attributes of the data object, and an indication whether a conflict occurred during processing of a request to access the data object.
Examples disclosed herein relate, among other things, to a method. The method may obtain a plurality of data entries stored in a memory, each data entry comprising a plurality of attribute values of a plurality of attributes; determine a set of segments, each segment being defined by a set of attribute values of a set of attributes; for each segment, determine a set of sub-segments, each sub-segment being defined by at least one additional attribute value of at least one additional attribute not in the set of attributes associated with the segment; for each sub-segment of each segment, compute a significance value associated with a change in the sub-segment's share within the segment; and determine a set of selected sub-segments and segments based on the significance value computed for each sub-segment of each segment.
Example implementations relate to privacy risk assessments. Some implementations may include a privacy risk identification engine to automatically identify privacy risks in an application based on an analysis of application code. Additionally, some implementations may include a privacy risk identification engine to obtain privacy risk information related to each of the privacy risks. Moreover, some implementations may include a privacy risk assessment engine to assess a severity of each of the privacy risks based on an analysis of the privacy risk information. In some examples, the analysis may include a determination of, for each of the privacy risks, a risk impact and a risk likelihood.
Examples relate to automated multi-credential assessment in a system. One example enables auditing an application by sending a first request for an action to be performed in the application, the first request based on a first privilege level, where the first privilege level corresponds with a first level of access to the application, and sending a second request for the action to be performed in the application, where the second request based on a second privilege level different from the first privilege level. The second privilege level may corresponds with a second level of access to the application different from the first level of access. The first request and second request may be performed, and the results of the performed first request and second request may be combined. The combined results may be made available.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06F 9/44 - Arrangements for executing specific programs
Examples relate to detecting vulnerabilities in a web application. One example enables identifying a set of inputs in a web application input form. The set of inputs may be categorized based on a set of predetermined conditions. The set of inputs may be scored based on the categorization. A subset of the set of inputs may be determined to be a set of parameters of interest for the web application based on the scored set of inputs.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
Examples disclosed herein relate to classification models for binary code data. Some of the examples enable obtaining changed binary code data and unchanged binary code data, and generating, using a machine-learning algorithm, a classification model based on training data that comprises the changed binary code data and the unchanged binary code data.
Examples herein disclose a facilitation of a direct connection between a source and a destination. In response to a request from the source to facilitate the direct connection, the examples identify the destination among multiple destinations based on available bandwidth from the multiple destinations. The examples transmit a security token to the identified destination for authentication of traffic from the source, wherein the source includes an indication of a period of time. Based on the indication of the period of time, the examples limit the direct connection.
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.
Example implementations relate to determining password modifications. Some implementations may include a password confirmation engine to identify an incorrect password entered during a current user authentication session. Additionally, some implementations may include a password comparison engine to determine whether the incorrect password is the same as a number of historical incorrect passwords. For example, each of the number of historical incorrect passwords may occur during a particular historical user authentication session of a plurality of historical user authentication sessions, and may be followed by an original correct password entered during the particular historical user authentication session. Additionally, some implementations may include a password modification engine to modify the original correct password to the incorrect password if the incorrect passwords as the same as each of the number of historical incorrect passwords.
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
In one example in accordance with the present disclosure, a method for dynamic schema typing may include receiving a host query with a function defining data to be retrieved. The function may include a dynamically definable schema. The method may also include receiving, at function invocation time, a data type schema defining a type of the data to be retrieved and generating a query using the data type schema as a value for the dynamically definable schema. The method may also include retrieving the data, converting the retrieved data into a form defined by the data type schema and providing the transformed data to the host query.
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.
Distances between geographic real-world entities modeled as geometric shapes are measured. At least two shapes are positioned in a coordinate space based on their respective sets of points. A non-empty quadrant set from a plurality of non-empty quadrant sets formed of non-empty quadrants in the coordinate space is identified such that the non-empty quadrants of the identified quadrant set associated with each of the at least two shapes satisfy a predetermined condition and a distance between portions of the at least two shapes in the non-empty quadrants of the identified non-empty quadrant set is an optimal value for the requested distance. The distance between the portions of the shapes in the identified quadrant set is determined to be the requested distance.
Disclosed are techniques for analyzing Information Technology (IT) trends. An example computing device includes a memory to store computer-readable instructions and a processor to execute the computer-readable instructions. The computer-readable instructions include a data collector to collect component usage data corresponding to a plurality of Information Technology (IT) components coupled to networks of a plurality of organizations, wherein the plurality of IT components comprise different types of IT components. The computer-readable instructions include an IT analytics module to analyze the component usage data to generate aggregated usage data corresponding to at least one of the different types of IT components and the plurality of organizations. The computer-readable instructions include a report generator to generate a report that includes at least a portion of the aggregated usage data and send the report to the user.
Provided is a process in which a plurality of requests is sent from an interface of a first device to an interface of a second device. If it is determined that at least some of the plurality of requests have not successfully been processed by the second device, an order of at least some of the not successfully processed requests is determined for resending, wherein the order is based on a fuzzy logic implementation.
Back end calls triggered by a user interaction with a client user interface may be identified. The user interaction may be correlated with a logic flow, and the logic flow may be associated with the back end calls. A supervised learning model may be trained using a labeled data set comprising the back end calls and their associated logic flow. Rules may be derived from the supervised learning model for classifying other back end calls. The rules may be outputted to a classifier that utilizes the rules to associate the other back end calls with the logic flow.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
92.
USING MACHINE LEARNING REGRESSION TO ESTIMATE TIME FOR COMPLETING APPLICATION LIFECYCLE MANAGEMENT WORK ITEM
A technique includes extracting data from a historical data store representing completed work items and associated features of the work items. The work items are associated with a lifecycle stage of an application. The technique includes training a regression model to estimate a time for completing a given work item based at least in part on the features.
Results of a replay of multiple scripts are received, each script of the multiple scripts including information relating to activities between at least one client device and at least one server device over a network. The results are compared, and based on the comparing, a difference between the scripts is identified.
Automated weighting is performed that includes transforming a behavior of each respective dimension of multiple dimensions of a selected group of events to a respective weight, the respective weight determined based on a distribution of values of the respective dimension, and where the weight determined for a first of the plurality of dimensions is greater than the weight determined for a second of the plurality of dimensions. Similarity values are computed indicating similarities between further events and the selected group of events, the similarity values based on a combination of the weights and distances between the further events and the selected group of events. Cohorts of the further events are generated by performing multi-level ranking that comprises ranking groups of the further events based on the similarity values, and applying merging to the groups to produce merged groups. The cohorts are visualized in a graphical visualization.
Example implementations relate to performance tracking in a security information sharing platform. For example, the security information sharing platform may enable sharing of security information among a plurality of users. A method of performance tracking in the security information sharing platform may include monitoring user behavior in the security information sharing platform, comparing the user behavior against a plurality of performance objectives in the security information sharing platform, and assigning a badge to a user among the plurality of users, for achievement of a performance objective among the plurality of performance objectives, based on the comparison.
Examples of implementations relate to privacy preservation in a security information sharing platform. For example, a system of privacy preservation comprises a physical processor that executes machine-readable instructions that cause the system to compare, in a security information sharing platform that enables sharing of security information among a plurality of users, a set of profiles wherein each profile is associated with an individual user; identify, based on the comparison, a profile element of a particular profile that is predictive of an identity of a user associated with the particular profile; and provide information about the profile element to the user of the particular profile.
In one example in accordance with the present disclosure, a method for threat score determination includes detecting a change in malicious activity for a security object. The method also includes identifying an indicator that provides contextual information for the security object and determining a linked resource that is associated with a database record of the security object. The method also includes determining a first threat score associated with the security object and determining a relationship between the linked resource and the security object. The method also includes determining a second threat score associated with the linked resource based on the indicator, the threat score of the linked object and the relationship between the linked resource and the security object.
According to an example, an index for three-dimensional geographic data stores information regarding a set of spherical polygons. The index includes position information of the vertices and edges of the set of spherical polygons in a coordinate system. At least a subset of the edges of the set of spherical polygons are represented as arcs within the index. Geo-positioning information can be determined based on the index.
A system includes a database client, and a distributed database comprising database nodes. The distributed database may receive a database query from the client, determine that the query comprises a range of hash values of a table partition stored by a node of the distributed database, and determine that the range of hash values is not stored by other nodes of the distributed database. Responsive to determining that the range of hash values of the query is stored by the node and not by the other nodes, the database may generate an optimized distributed execution plan that includes the node that stores the range of hash values and excludes the nodes that do not include the range of hash values.
Examples for graph database query classification include receiving a graph query and determining if the graph query matches benchmark data. In the event that the graph query does not match benchmark data, the query may be parsed, a canonical internal representation of the query may be determined, the representation may be mapped to a rule, and the query may be classified based on the rule. In the event that the confidence score for the query classification does not exceed a threshold, the query may be sent to a synthetic graph or synopsis for simulation. In some examples, the simulation may include selecting computationally expensive graph operators in the query for simulation.
