International Business Machines Corporation

Leigh Allen Williamson

Vijay Ekambaram

Shinoj Zacharias

Roger Snook

David Yu Chang

Rohith Kottamangalam Ashok

Vishwanath Venkataramappa

Barry Charles Searle

Ping Wang

Howard Neil Anglin

Saurabh Sinha

Jason Robert McGee

Christopher Creighton Mitchell

Satish Chandra

Christopher Paul Vignola

Ricardo N Olivieri

Donald Alexander Bourne

William Mitchell Edwards

Suresh Thummalapenta

Michael Cheng

Hany A Salem

Qinhua Wang

Charles James Redlin

Michael J Casile

Yandrapally Rahulkrishna

Alexander D Lewitt

Tom Zhongyu Zhou

Jonathan A Cagadas

Simon D Mikulcik

Karan Shukla

Nitin Gaur

Ching-Yun Chao

Sumit Patel

Ajay Arvind Apte

Hyen Vui Chung

Zhennan Wang

Gautham Pamu

Thomas Edward Musta

Carlton Keith Mason

Shawn William Lauzon

Jinfang Chen

Sreedhar Reddy Arda

Domingo S Hidalgo

Ching-Yun Yang

Edward McCarthy

Roger L Cundiff, Jr

Lance Jeffery Purple

Benjamin S Vera-Tudela

Natasha Pothen

Juniarti A Suryakusuma

Christopher M Crane

Jesus Alva

Aaron Lee

Marietta Johnston

Kellin McAvoy

Michael-James Parsons

Lakshminarayanan Krishm

Todd Michael Wiese

Lance Jeffrey Purple

Embodiments of the invention provide for malware collusion detection in a mobile computing device. In one embodiment, a method for malicious inter-application interaction detection in a mobile computing device includes filtering applications installed in a mobile device to a set of related applications and then monitoring in the mobile device execution of the related applications in the set. The method additionally includes computing resource utilization of one of the related applications executing in a background of the mobile device while also computing execution performance of a different one of the related applications. Finally, the method includes responding to a determination that the computed resource utilization is high while the computed execution performance is poor by generating a notification in the display of the mobile device that the one of the related applications is suspected of malware collusion with the different one of the related applications.

Malware collusion detection

Embodiments generally relate to improving education and training sessions. In some embodiments, a method includes determining an attentiveness level associated with a first user during a learning session. The method further includes determining one or more drops in the attentiveness level during the learning session. The method further includes tracking inattentiveness information associated with the one or more drops in the attentiveness level. The method further includes performing one or more corrective actions in response to the one or more drops in the attentiveness level based at least in part on the inattentiveness information.

Education and training sessions

Embodiments are directed to a system, computer program product, and method for dynamic object mining. A received file is segmented, re-formatted, and organized into buffers, while maintaining the order of the received file. Multiple buffers are processed in parallel for object mining. A listener is encoded into the buffers to support asynchronous processing, and more specifically ordering of mined objects. An output file of the mined objected or associated frames is created. The file is populated with a sequential ordering of the objects that follows the order of the received file.

Object mining and recognition system

An example operation may include one or more of authorizing a blockchain for a video file, generating a first tracking value for an entry block referencing the video file, the first tracking value generated based on first data and the video file, receiving second data for each of additional blocks in the blockchain, generating second tracking values based on the second data of the additional blocks, forming the additional blocks including the second tracking values, respectively, appending the additional blocks to the entry block, the entry block and the additional blocks cryptographically linked in an ordered sequence, each of the additional blocks referencing a version of the video file which corresponds to an original version of the video file as referenced by the entry block or a processed version of the video file, the second data in each of the additional blocks indicative of processing performed on the version of the video file in that block, and tracing through the blockchain based on the first and second tracking values to confirm an auditable and immutable chain-of-custody of the video file.

Chain-of-custody of digital content in a database system

An approach for generating a modified software application by leveraging a customized advertisement between application owner and application vendor. The approach includes identifying a software application from a software application store repository and retrieving information from the software application store repository associated with the software application. The approach creates a requirement based on the retrieved information and identifying one or more vendor based on the requirement. After creating the targeted advertisement aimed at an application owner based on the identified vendor then the approach launches the advertisement campaign. The approach receives the notification from the application owner based on the advertisement to engage the identified vendor and modifies the software application based on the requirement.

Application modification using software services

As part of identifying a theme corresponding to a wireframe, the wireframe comprising a set of graphical elements is analyzed, the set of graphical elements specifying a graphical representation of a user interface of a front-end application. A similarity measure is computed, the similarity measure quantifying a degree of similarity between the theme and an entry in a feature implementation history stored in a code repository. From the entry, a first feature to be implemented in a back-end application is extracted, the first feature servicing a data request from the front-end application. A source code stub extracted from the code repository, comprising a partial implementation of the first feature in the back-end application, is coupled with a first graphical element in the set of graphical elements in the wireframe.

Back-end application code stub generation from a front-end application wireframe

A computer-implemented method comprising receiving and analyzing a data point to determine parameters of the data point, generating an alert ticket based on the analysis of the data point, communicating, via a chatbot, at least some information contained in the alert ticket to one or more users, and categorizing, via the chatbot, the data point that resulted in the alert ticket based on behavior of a device that generated the data point.

Supervised learning system training using chatbot interaction

An apparatus comprises a memory and a processor coupled to the memory. The processor is configured to receive input from a cloud service data source, wherein the input comprises at least one data point, analyze the data point via a machine learning model to determine characteristics indicated by the data point, determine whether the characteristics indicated by the data point meet an alert threshold that indicates a problem in a network, generate an alert ticket when the characteristics indicated by the data point meet the alert threshold, wherein the alert ticket indicates the problem in the network, communicate with a user based on contents of the alert ticket, receive feedback from the user relating to the alert ticket, and train the machine learning model according to the feedback received from the user.

A method, computer program product, and a system where a processor(s) monitors activities of a user operating a vehicle with a navigational device providing route guidance, via a first route, to a geographic destination. The processor(s) identifies driving patterns of the user to generate a driver profile for the user, including a baseline activity pattern. The processor(s) determines, at a given time, that the monitored activities of the user are outside of the baseline activity pattern and enables an integrated image capture device in the vehicle and captures images of an environment outside of a the vehicle. The processor(s) utilizes the image capture device, to capture images and derives data describing environmental conditions, based on performing a semantic analysis of the images. The processor(s) transmits the data to a repository and generates a second route to the destination, based on the driver profile and relevant data from the repository data.

Spatio-temporal re-routing of navigation

Disclosed aspects relate to debugging a set of code components of an application program. A set of defect data which indicates a set of defects may be collected with respect to an application program. The set of defect data may be derived from a set of post-compilation users of the application program. A set of test case data which indicates a set of user interface features of the application program may be collected with respect to the application program. The set of test case data may be derived from a set of development tests of the application program. Using both the set of defect data and the set of test case data, a set of fragility data for the set of code components of the application program may be determined. Based on the set of fragility data, the set of code components of the application program may be debugged.

Code component debugging in an application program

Runtime system statistics logging is dynamically controlled at code and application levels, based on user reviews. Logging of specific code components in specific application instances, identified based on user reviews, is automatically turned on, based on the user reviews indicating defects. Logging for other components or application instances, however, remains off or is automatically turned off.

Dynamically controlling runtime system logging based on end-user reviews

A computer-implemented facility is provided for intelligent mobile device selection for mobile application testing. The computer-implemented facility determines features of a new mobile application to be tested, and compares the features of the new mobile application with features of multiple known mobile applications to identify one or more known mobile applications with similar features. Based at least in part on automated analysis of user reviews of the one or the more known mobile applications operating in one or more types of mobile devices, the facility provides one or more risk scores for operation of the new mobile application in the one or more types of mobile devices. Further, based on the risk scores, a recommended set of mobile devices in which to test the new mobile application may be generated for use in testing the new mobile application.

Intelligent device selection for mobile application testing

Growing community of inventors

Austin, TX, United States of America

Leigh Allen Williamson