Abstract: A computer-implemented method, system and computer program product for identifying and recommending code snippets to be reused by a software developer. Multiple sources (e.g., social media messages) are monitored for source code development difficulties. In response to detecting a source code development difficulty, the source code which is the source of the detected source code development difficulty is labeled with a struggle tag, which may indicate a category of the source code. Furthermore, source code snippets from various sources (e.g., source code snippets publicly available on the Internet) are mined, identified and labeled with tags, which may indicate a category of the source code snippet. Relevant source code snippets are identified to address the software developer's struggles based on matching one or more of these tags with the struggle tag, which are later ranked as to which are the best to address the software developer's struggles in writing source code.

Abstract: An object detection algorithm is selectively applied to frames in a video. A frame in the video is analyzed using a set of neural networks of the object detection algorithm to detect a location of an object in the frame and predict a bounding box for the object in the frame. A magnitude of a delta between the frame and a second frame is determined. The magnitude of the delta is determined based on a difference between the two frames in values of at least one parameter of their respective sets of pixels. Responsive to the magnitude of the delta being less than a threshold, a new bounding box is predicted for the object in the second frame without analyzing the second frame using the set of neural networks.

Abstract: A method, system, and computer program product for determining selection parameters for filtering algorithms using environmental data of images. The method may include receiving an image. The method may also include analyzing the image using at least image processing. The method may also include identifying, based on the analyzing, image data and environmental data of the image. The method may also include inputting the image data and the environmental data into a machine learning algorithm, where the machine learning algorithm includes mapped relationships between at least the environmental data and selection parameters. The method may also include predicting, using the machine learning algorithm, optimal selection parameters for the image. The method may also include applying the optimal selection parameters to a filtering algorithm for the image. The system and computer program product may include similar steps.

Abstract: An example operation may include one or more of connecting, by a miner node, to a blockchain network comprised of a plurality user nodes, reading, by the miner node, a smart contract generated by a user node of the plurality of the user nodes, wherein the smart contract specifies a location of a user video file, acquiring, by the miner node, the user video file from the location, generating, by the miner node, a profile vector of the user video file based on properties of the user video file, determining, by the miner node, a cluster that has similar profile vectors, applying, by the miner node, a compression algorithm associated with the cluster to the user video file to generate a compressed user video file, and sending, by the miner node, the compressed user video file to the user node of the plurality of the user nodes for a validation.

Abstract: A method, and associated computer system and computer program product, for video compression that includes receiving a video file including a plurality of frames, identifying at least one image feature in each of the plurality of frames, determining a semantic state change of the image feature for each successive frame after a first of the plurality of frames, and storing the first of the plurality of frames and the semantic change of the image feature for each successive frame after the first of the plurality of frames.

Abstract: A processor(s) processes multiple image frames of a scene for object recognition by applying a first process of a plurality of visual recognition processes using different grid sizes, to one or more image frames. The first process divides the image frame(s) into an m×m grid to determine bounding boxes for identifying one or more objects. Based on detecting passage of a predefined time interval, less than a period of time represented by the image frames, processing dynamically switches to a second process to process one or more other image frames. The second process is applied to the other image frame(s), dividing the other image frame(s) into an n×n grid to determine bounding boxes for identifying one or more other objects in the other image frame(s), where the n×n grid used by the second process is different from the m×m grid.

Abstract: A system can determine if a server is overloaded and unable to process a request selected for transmission by a user. If the server is unable to process the request, the system can present buffer tasks for the user to complete while the system repeatedly pings the server, checking to see if the server is still unable to process the request. If the system detects that the server is not overloaded or no longer is overloaded, the system sends the request. The system can determine a priority of the request and adjust buffer tasks based on the priority.

Abstract: An object detection algorithm is selectively applied to frames in a video. A frame in the video is analyzed using a set of neural networks of the object detection algorithm to detect a location of an object in the frame and predict a bounding box for the object in the frame. A magnitude of a delta between the frame and a second frame is determined. The magnitude of the delta is determined based on a difference between the two frames in values of at least one parameter of their respective sets of pixels. Responsive to the magnitude of the delta being less than a threshold, a new bounding box is predicted for the object in the second frame without analyzing the second frame using the set of neural networks.

Abstract: The approach is for cognitively transferring IoT configurations between similar IoT ecosystems based on several factors. The approach scans IoT devices from a first and a second IoT ecosystem and extracts user data from the first and the second IoT ecosystem. The approach compares the first IoT ecosystem against the second IoT ecosystem and determines if the first IoT ecosystem is similar to the second IoT ecosystem. If the first and second IoT ecosystem are similar, then transfer data from the first IoT ecosystem to the second IoT ecosystem.

Abstract: Methods and program products repositioning icons on a touch display based on characteristics of touch gestures performed on the touch display. The disclosed method may include detecting characteristics of a touch gesture performed on a touch display of an electronic device, and determining if the detected characteristics of the touch gesture exceed a predetermined threshold. In response to determining the detected characteristics exceed the predetermined threshold, the method may also include selecting a group of icons based on at least one of the detected characteristics of the touch gesture performed on the touch display, or predetermined electronic device data. Additionally, the method may include repositioning the selected group of icons on the touch display.

Abstract: Approaches presented herein enable optimization of a developing application to a user base. More specifically, application-centric data is gathered during a cultivation phase of the developing application. Substantially concurrently with the cultivation phase of the developing application, the application-centric data is analyzed according to static code of the developing application, a testing of the developing application, or a user experience (UX) design of the developing application. A machine learning model is applied to the analyzed application-centric data. This machine learning model is trained on historic application feedback data from applications available to the user base. Based on the machine learning model, a recommended change to optimize the developing application to the user base is generated.

Abstract: Systems and methods for preventing software application tampering are disclosed. 1. In embodiments, a computer-implemented method includes: identifying, by a computing device, an IP related software code segment of a software application; segregating, by the computing device, the IP related software code segment into one or more native code clusters and one or more non-native code clusters; refactoring, by the computing device, the one or more non-native code clusters into one or more stand-alone portable components (SPCs); determining, by the computing device, code migration scores for the one or more SPCs; and determining, by the computing device, select SPCs from the one or more SPCs to migrate to a remote security server based on the code migration scores.

Abstract: A method, computer program product, and a system where a processor(s) identifies a partial signature in a code snippet from a source. The processor(s) data mines, based on the partial signature, websites and repositories to identify and extract metadata relevant to the source. The processor(s) generates, based on a first portion of the metadata, an installation package to install the code base for execution in a test environment of a target computing system. The processor(s) generates, based on a second portion of the metadata, an access contract (expected accesses of the code snippet) during execution on the target system. The processor(s) deploys the installation package in the test environment and executes the code base.

Abstract: A processor(s) processes multiple image frames of a scene for object recognition by applying a first process of a plurality of visual recognition processes using different grid sizes, to one or more image frames. The first process divides the image frame(s) into an m×m grid to determine bounding boxes for identifying one or more objects. Based on detecting passage of a predefined time interval, less than a period of time represented by the image frames, processing dynamically switches to a second process to process one or more other image frames. The second process is applied to the other image frame(s), dividing the other image frame(s) into an n×n grid to determine bounding boxes for identifying one or more other objects in the other image frame(s), where the n×n grid used by the second process is different from the m×m grid.

Abstract: The approach is for cognitively transferring IoT configurations between similar IoT ecosystems based on several factors. The approach scans IoT devices from a first and a second IoT ecosystem and extracts user data from the first and the second IoT ecosystem. The approach compares the first IoT ecosystem against the second IoT ecosystem and determines if the first IoT ecosystem is similar to the second IoT ecosystem. If the first and second IoT ecosystem are similar, then transfer data from the first IoT ecosystem to the second IoT ecosystem.

Abstract: A system can determine if a server is overloaded and unable to process a request selected for transmission by a user. If the server is unable to process the request, the system can present buffer tasks for the user to complete while the system repeatedly pings the server, checking to see if the server is still unable to process the request. If the system detects that the server is not overloaded or no longer is overloaded, the system sends the request. The system can determine a priority of the request and adjust buffer tasks based on the priority.

Abstract: Container orchestration software that controls instantiation, deployment, revision and removal of containers located in more than one cloud (that is, each container is located in a single cloud, but the set of orchestrated containers includes containers in more than one cloud). In some embodiments, the container orchestration software takes the form of a master node. In some embodiments, the container orchestration software takes the form of a Kubernetes type master node.

Abstract: A method, computer program product, and a system where a processor(s) identifies a partial signature in a code snippet from a source. The processor(s) data mines, based on the partial signature, websites and repositories to identify and extract metadata relevant to the source. The processor(s) generates, based on a first portion of the metadata, an installation package to install the code base for execution in a test environment of a target computing system. The processor(s) generates, based on a second portion of the metadata, an access contract (expected accesses of the code snippet) during execution on the target system. The processor(s) deploys the installation package in the test environment and executes the code base.

Abstract: A system, method and program product for processing software development kits (SDKs). A system is provided that includes a container creation system that wraps an inputted SDK in a penalty container to create a wrapped SDK, wherein the wrapped SDK includes re-instrumented code that redirects application programming interface (API) calls between the SDK and an associated App and operating system through a wrapper in order to monitor runtime behavior; wherein the container creation system instantiates a policy module that is configurable by an SDK provider and App developer to implement a mutual policy configuration for the penalty container; and wherein the penalty container includes a penalty manager notifies the SDK provider and App developer of SDK violations of the mutual policy configuration.

Abstract: Aspects of the present invention provide a software defined radio (SDR) system and/or an approach for enabling heterogeneous device communications using a SDR device in an internet of things (IoT) environment. In an embodiment, the SDR device (e.g., a network adapter thereof) obtains a set of communications sent by an IoT device. Based on an analysis of the set of communications by an SDR bot running on the SDR device, a protocol of the set of communications is determined. The SDR bot interprets a communication of the set of communications, and, based on the interpreted communication, a notification is forwarded by the SDR device (e.g., by the network adapter). This notification can include an instruction to another IoT device to perform a task, a report based on communications from multiple IoT devices, or the like.