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Definitions

• the present invention relates generally to information processing and, in particular, to data augmentation for image classification tasks.
• Data augmentation is a technique used in certain applications.
• data augmentation involves applying a small mutation to training images for better generalization performance by avoiding overfitting.
• a computer-implemented method for performing machine learning for an image classification task.
• the method includes selecting, by a processor operatively coupled to one or more databases, a first and a second image from one or more training sets in the one or more databases.
• the method further includes overlaying, by the processor, the second image on the first image to form a mixed image, by averaging an intensity of each of a plurality of co-located pixel pairs in the first and the second image.
• the method also includes training, by the processor, a machine learning process configured for the image classification task using the mixed image to augment data used by the machine learning process for the image classification task.
• a computer program product for performing machine learning for an image classification task.
• the computer program product includes a non- transitory computer readable storage medium having program instructions embodied therewith.
• the program instructions are executable by a computer to cause the computer to perform a method.
• the method includes selecting, by a processor operatively coupled to one or more databases, a first and a second image from one or more training sets in the one or more databases.
• the method further includes overlaying, by the processor, the second image on the first image to form a mixed image, by averaging an intensity of each of a plurality of co-located pixel pairs in the first and the second image.
• the method also includes training, by the processor, a machine learning process configured for the image classification task using the mixed image to augment data used by the machine learning process for the image classification task.
• a computer processing system for performing machine learning for an image classification task.
• the computer processing system includes a processor that is operatively coupled to one or more databases.
• the processor is configured to select a first and a second image from one or more training sets in the one or more databases.
• the processor is further configured to overlay the second image on the first image to form a mixed image, by averaging an intensity of each of a plurality of co-located pixel pairs in the first and the second image.
• the processor is also configured to train a machine learning process for the image classification task using the mixed image to augment data used by the machine learning process for the image classification task.
• an advanced driver-assistance system for a motor vehicle.
• the advanced driver-assistance system includes a camera configured to capture an actual image relating to an external view from the motor vehicle.
• the advanced driver-assistance system further includes a processor that is operatively coupled to one or more databases.
• the processor is configured to select a first and a second image from one or more training sets in the one or more databases.
• the processor is further configured to overlay the second image on the first image to form a mixed image, by averaging an intensity of each of a plurality of co-located pixel pairs in the first and the second image.
• the processor is also configured to perform machine learning by training a machine learning process configured for an image classification task using the mixed image to augment data used by the machine learning process for the image classification task.
• the image classification task relates to a driver-assistance function.
• the processor is additionally configured to apply the trained machine learning process to the test image to obtain a classification for the test image.
• the processor is also configured to control a function of one or more hardware devices of the motor vehicle, responsive to the classification for the test image.
• FIG. 1 shows an exemplary processing system to which the present invention may be applied, in accordance with an embodiment of the present invention
• FIG. 2 shows an exemplary system to which the present invention can be applied, in accordance with an embodiment of the present invention
• FIGs. 3-4 show an exemplary method for data augmentation for image classification tasks, in accordance with an embodiment of the present invention
• FIGs. 5-6 show another exemplary method for data augmentation for image classification tasks, in accordance with an embodiment of the present invention
• FIG. 7 shows an overall neural network training scheme to which the present invention can be applied, in accordance with an embodiment of the present invention
• FIG. 8 is a block diagram showing an illustrative cloud computing environment having one or more cloud computing nodes with which local computing devices used by cloud consumers communicate, in accordance with an embodiment of the present invention.
• FIG. 9 is a block diagram showing a set of functional abstraction layers provided by a cloud computing environment, in accordance with an embodiment of the present invention.
• the present invention is directed to data augmentation for image classification tasks.
• the present invention can achieve a higher accuracy in image classification tasks with a Neural Network (NN) by introducing a new data augmentation technique.
• NN Neural Network
• the present invention is not limited to neural networks and can be used with any learning mechanism/technique, as readily appreciated by one of ordinary skill in the art given the teachings of the present invention provided herein.
• the present invention is not limited to any particular type of neural network and, thus, can be used with neural network such as Convolutional Neural Networks, Recurrent Neural Networks (RNNs), and so forth.
• the present invention can also be applied to non-NN based learning mechanisms/techniques including, but not limited to, Inductive Logic Programming (ILP), decision trees, and so forth.
• ILP Inductive Logic Programming
• FIG. 1 shows an exemplary processing system 100 to which the invention principles may be applied, in accordance with an embodiment of the present invention.
• the processing system 100 includes at least one processor (CPU) 104 operatively coupled to other components via a system bus 102.
• processor CPU
• a cache 106 operatively coupled to the system bus 102.
• ROM Read Only Memory
• RAM Random Access Memory
• I/O input/output
• sound adapter 130 operatively coupled to the system bus 102.
• network adapter 140 operatively coupled to the system bus 102.
• user interface adapter 150 operatively coupled to the system bus 102.
• display adapter 160 operatively coupled to the system bus 102.
• graphics processing Unit (GPU) 194 is operatively coupled to the system bus 102.
• a first storage device 122 and a second storage device 124 are operatively coupled to system bus 102 by the I/O adapter 120.
• the storage devices 122 and 124 can be any of a disk storage device (e.g., a magnetic or optical disk storage device), a solid state magnetic device, and so forth.
• the storage devices 122 and 124 can be the same type of storage device or different types of storage devices.
• a speaker 132 is operatively coupled to system bus 102 by the sound adapter 130.
• a transceiver 142 is operatively coupled to system bus 102 by network adapter 140.
• a display device 162 is operatively coupled to system bus 102 by display adapter 160.
• a first user input device 152, a second user input device 154, and a third user input device 156 are operatively coupled to system bus 102 by user interface adapter 150.
• the user input devices 152, 154, and 156 can be any of a keyboard, a mouse, a keypad, an image capture device, a motion sensing device, a microphone, a device incorporating the functionality of at least two of the preceding devices, and so forth. Of course, other types of input devices can also be used, while maintaining the spirit of the present invention.
• the user input devices 152, 154, and 156 can be the same type of user input device or different types of user input devices.
• the user input devices 152, 154, and 156 are used to input and output information to and from system 100.
• processing system 100 may also include other elements (not shown), as readily contemplated by one of skill in the art, as well as omit certain elements.
• various other input devices and/or output devices can be included in processing system 100, depending upon the particular implementation of the same, as readily understood by one of ordinary skill in the art.
• various types of wireless and/or wired input and/or output devices can be used.
• additional processors, controllers, memories, and so forth, in various configurations can also be utilized as readily appreciated by one of ordinary skill in the art.
• system 200 described below with respect to FIG. 2 is a system for implementing respective embodiments of the present invention. Part or all of processing system 100 may be implemented in one or more of the elements of system 200. [0019] Further, it is to be appreciated that processing system 100 may perform at least part of the method described herein including, for example, at least part of method 300 of FIGs. 3-4 and/or at least part of method 500 of FIGs. 5-6 and/or at least part of method 700 of FIG. 7. Similarly, part or all of system 200 may be used to perform at least part of method 300 of FIGs. 3-4 and/or at least part of method 500 of FIGs. 5-6 and/or at least part of method 700 of FIG. 7.
• FIG. 2 shows an exemplary system 200 to which the present invention can be applied, in accordance with an embodiment of the present invention.
• the system 200 includes a computer processing system 210 (e.g., computer processing system 100) and a set of other computer processing systems 220.
• a computer processing system 210 e.g., computer processing system 100
• a set of other computer processing systems 220 e.g., one or more of the computer processing systems 210 and 220 can be configured as servers.
• the computer processing system 210 can be configured to receive images from any of the other computer processing systems 220.
• the computer processing system 210 can subject the received images to a data augmentation technique for image classification tasks in accordance with a preferred embodiment of the present invention.
• the results of the data augmentation technique can then be provided from the computer processing system 210 to one or more of the other computer processing systems 220. In this way, a more accurate image classification as compared to the prior art can be achieved in accordance with the teachings of preferred embodiments of the present invention.
• Each of the computer processing system 210 and the other computer processing systems 220 at least include a processor 291 , a memory 292, and a transceiver 293. Also, at least one of the computer processing systems such as 210 can include a camera 299 for capturing an actual image to which a trained neural network can be applied. Moreover, at least the other computer processing systems 220 further include a database 294 for storing all or a portion of one or more training (image) sets. The transceiver 293 of the other computer processing systems 220 send the images to the transceiver 293 of the computer processing system 210. The processor 291 and memory 292 of the computer processing system 210 then processing the images to provide an image classification result to one or more of the other computer processing systems 220 via the transceivers 293.
• computer processing system 210 can be part of another system 271.
• Such other system can be, for example, but is not limited to, a surveillance system, a computer vision system, an action recognition system, an Advanced Driver-Assistance System, and so forth.
• a surveillance system can be, for example, but is not limited to, a computer vision system, an action recognition system, an Advanced Driver-Assistance System, and so forth.
• the preceding types of systems are merely illustrative and the present invention can be applied to a myriad of different types of systems that can benefit from image classification.
• Other elements in these systems are not shown in FIG. 2 for the sake of brevity and clarity, but are nonetheless readily known and appreciated by one of ordinary skill in the art.
• the elements thereof are interconnected by a network(s) 201.
• At least one of the elements of system 200 is processor-based (in the shown example, all are processor-based). Further, while one or more elements may be shown as separate elements, in other embodiments, these elements can be combined as one element. The converse is also applicable, where while one or more elements may be part of another element, in other embodiments, the one or more elements may be implemented as standalone elements. Moreover, one or more elements of FIG. 2 can be implemented in a cloud configuration including, for example, in a distributed configuration. Additionally, one or more elements in FIG.
• DSP Digital Signal Processing
• ASIC Application Specific Integrated Circuit
• FPGA Field Programmable Gate Array
• CPLD Complex Programmable Logic Device
• method 300 of FIGs. 3-4 corresponds to a data augmentation technique that is described using only two input images that form a single image pair for the sake of simplicity and illustration.
• method 500 of FIGs. 5-6 corresponds to the data augmentation technique of FIG. 3 applied to a set of input image pairs that include more than one pair of input images.
• the method steps described herein can be performed by, e.g., a Central Processing Unit (CPU) (e.g., CPU 104 of FIG. 1) and/or by a Graphics Processing Unit (GPU) (GPU 194 of FIG. 1).
• CPU Central Processing Unit
• GPU Graphics Processing Unit
• the methods 300, 500, and 700 refer to the use of neural networks, any type of machine learning process can be used in place thereof, as readily appreciated by one of ordinary skill in the art.
• the present invention can be used with machine learning processes including, but not limited to, decision tree learning, association rule learning, deep learning, inductive programming logic, support vector machines, clustering, Bayesian networks, reinforcement learning, representation learning, similarity and metric learning, sparse dictionary learning, rule-based machine learning, and learning classification.
• machine learning processes including, but not limited to, decision tree learning, association rule learning, deep learning, inductive programming logic, support vector machines, clustering, Bayesian networks, reinforcement learning, representation learning, similarity and metric learning, sparse dictionary learning, rule-based machine learning, and learning classification.
• the preceding machine learning processes to which the present invention can be applied are merely illustrative and, thus, the present invention can also be applied to other machine learning processes, while maintaining the spirit of the present invention.
• FIGs. 3-4 show an exemplary method 300 for data augmentation for image classification tasks, in accordance with an embodiment of the present invention.
• step 305 select a first input image (hereinafter "first image”) and a second input image (hereinafter "second image”) from a training set.
• first image a first input image
• second image a second input image
• step 310 overlay the second image on the first image by averaging an intensity of each of a plurality of co-located pixel pairs in the first and the second image to form a mixed image.
• step 315 train an image classifier, implemented as neural network, using the mixed image.
• step 315 can include one or more of steps 315A-C.
• step 315A train the image classifier by using the label of the first image as a label of the mixed image.
• step 315B train the image classifier by using the label of the second image as a label of the mixed image.
• step 315C train the image classifier by mixing a label of the first image with a label of the second image to form a label of the mixed image.
• step 320 continue training using the mixed image, for example, until a predetermined criteria has been met.
• the predetermined criteria can include, but is not limited to, for example, improvements in accuracy or training loss for training data or validation data.
• training can be stopped when there is no further improvement in classification accuracy by performing further training.
• step 325 perform fine tuning of the image classifier without mixing the second image. That is, perform fine tuning using the unmixed images, i.e., the first image and the second image.
• step 330 receive an image to be classified.
• step 335 apply the trained neural network to the image to be classified.
• step 340 output a classification for the image to be classified.
• response action performs an action in response (hereinafter interchangeably referred to as "response action") to the classification for the image.
• the response action can be related to any number of applications to which the present invention can be applied including, but not limited to, surveillance, action recognition, an Advanced Driver-Assistance System, computer vision, and so forth.
• the action can be, for example, generating an audible reproduction of the classification (e.g., in the case of computer vision), generating a user- perceptible alert, locking a door to keep an object (e.g., a person or other animate object and/or an inanimate object) contained, unlocking a door to release a contained object, suggesting a correct action to a user (e.g., responsive to a classification indicating an incorrect action is being performed by the user, e.g., in a documented procedure and/or other process), taking control over one or more vehicle functions (e.g., braking, accelerating, steering), and so forth.
• the classification can correspond to a prohibited and/or dangerous item and/or so forth. It is to be appreciated that the preceding response actions are merely illustrative and, thus, any other response actions could also be performed responsive to a classification made by the present invention, while maintaining the spirit of the present invention.
• FIGs. 5-6 show another exemplary method 500 for data augmentation for image classification tasks, in accordance with an embodiment of the present invention.
• step 505 select a set of one or more different input image pairs from a training set of images.
• Each of the image pairs in the set includes a first respective input image (hereinafter “first image”) and a second respective input image (hereinafter “second image”) selected from the set.
• first image first respective input image
• second image second respective input image
• step 510 for each image pair, overlay the second image on the first image by averaging an intensity of each of a plurality of co-located pixel pairs in the first and the second image to form a mixed image from each of the image pairs.
• step 515 train an image classifier, implemented as neural network, using the mixed images formed from each of the image pairs.
• step 515 can include one or more of steps 515A-C.
• step 515A train the image classifier by using the label of the first image as a label of the mixed image.
• step 515B train the image classifier by using the label of the second image as a label of the mixed image.
• step 515C train the image classifier by mixing a label of the first image with a label of the second image to form a label of the mixed image.
• step 520 continue training using the mixed images, for example, until a predetermined criteria has been met.
• the predetermined criteria can include, but is not limited to, for example, improvements in accuracy or training loss for training data or validation data. Thus, for example, training can be stopped when there is no further improvement in classification accuracy by performing further training.
• step 525 perform fine tuning of the image classifier without mixing the second images. That is, perform fine tuning using the unmixed images, i.e., the first images and the second images of the image pairs.
• step 530 receive an image to be classified.
• step 535 apply the trained neural network to the image to be classified.
• step 540 output a classification for the image to be classified.
• step 545 perform an action in response to the classification for the image. Possible exemplary actions are further described above with respect to step 345 of method 300.
• FIG. 7 shows an overall neural network training scheme to which the present invention can be applied, in accordance with an embodiment of the present invention.
• step 705 train the neural network in a training stage.
• step 705 can include one or more of steps 705A-C.
• step 705A access a data augmentation technique to make the data augmentation technique available for use on an as needed or as called for basis.
• the data augmentation technique can be any of method 300 of FIGs. 3-5 and method 500 of FIGs. 5-6.
• step 705B disable the data augmentation technique at the beginning of the training stage, for example for a number of epochs. In this way, overall neural network training speed is increased and a better final accuracy can be obtained (as proven through numerous experiments).
• step 705C disable the data augmentation technique at one or more intermediate time periods of the training stage.
• the data augmentation technique can be disabled for a number of epochs.
• overall neural network training speed is increased and a better final accuracy can be obtained.
• Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service.
• This cloud model may include at least five characteristics, at least three service models, and at least four deployment models.
• On-demand self-service a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service's provider.
• Resource pooling the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter).
• Rapid elasticity capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.
• Measured service cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service.
• level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts).
• SaaS Software as a Service: the capability provided to the consumer is to use the provider's applications running on a cloud infrastructure.
• the applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail).
• a web browser e.g., web-based e-mail
• the consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.
• PaaS Platform as a Service
• the consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations.
• laaS Infrastructure as a Service
• the consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).
• Private cloud the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises.
• Public cloud the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.
• Hybrid cloud the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds).
• a cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability.
• An infrastructure that includes a network of interconnected nodes.
• cloud computing environment 850 includes one or more cloud computing nodes 810 with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone 854A, desktop computer 854B, laptop computer 854C, and/or automobile computer system 854N may communicate.
• Nodes 810 may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof.
• This allows cloud computing environment 850 to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device.
• computing devices 854A-N shown in FIG. 8 are intended to be illustrative only and that computing nodes 810 and cloud computing environment 850 can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).
• FIG. 9 a set of functional abstraction layers provided by cloud computing environment 850 (FIG. 8) is shown. It should be understood in advance that the components, layers, and functions shown in FIG. 9 are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided:
• Hardware and software layer 960 includes hardware and software components.
• hardware components include: mainframes 961 ; RISC (Reduced Instruction Set Computer) architecture based servers 962; servers 963; blade servers 964; storage devices 965; and networks and networking components 966.
• software components include network application server software 967 and database software 968.
• Virtualization layer 970 provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers 971 ; virtual storage 972; virtual networks 973, including virtual private networks; virtual applications and operating systems 974; and virtual clients 975.
• management layer 980 may provide the functions described below.
• Resource provisioning 981 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment.
• Metering and Pricing 982 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses.
• Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources.
• User portal 983 provides access to the cloud computing environment for consumers and system administrators.
• Service level management 984 provides cloud computing resource allocation and management such that required service levels are met.
• Service Level Agreement (SLA) planning and fulfillment 985 provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.
• SLA Service Level Agreement
• Workloads layer 990 provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation 991 ; software development and lifecycle management 992; virtual classroom education delivery 993; data analytics processing 994; transaction processing 995; and data augmentation for image classification tasks 996.
• the present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration.
• the computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.
• the computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device.
• the computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
• a non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing.
• a computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating
• electromagnetic waves electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
• Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network.
• the network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers.
• a network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
• Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as SMALLTALK, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages.
• the computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
• the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
• electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.
• These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
• These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
• the computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
• each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s).
• the functions noted in the block may occur out of the order noted in the figures.
• two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
• such phrasing is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of the third listed option (C) only, or the selection of the first and the second listed options (A and B) only, or the selection of the first and third listed options (A and C) only, or the selection of the second and third listed options (B and C) only, or the selection of all three options (A and B and C).
• This may be extended, as readily apparent by one of ordinary skill in this and related arts, for as many items listed.

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