Abstract: Data processing is disclosed including acquiring a first target image to be embedded with information and to-be-embedded information, acquiring, using the first target image, a second target image corresponding to the first target image, the second target image corresponding to an image including low-luminance pixels in the first target image, the low-luminance pixels being pixels having a luminance no higher than a luminance threshold value in the first target image, selecting candidate image areas from the second target image, determining a target embedding position for the to-be-embedded information in the first target image based on the candidate image areas, and embedding the to-be-embedded information in the target embedding position in the first target image.

Abstract: Systems, methods, and non-transitory computer-readable media can receive a set of video frames associated with a video. A determination can be made that a first set of consecutive video frames of the set of video frames depicts identical content to a second set of consecutive video frames of the set of video frames, wherein the first set of consecutive video frames and the second set of consecutive video frames satisfy a threshold number of consecutive video frames. The video is identified as a looping video based on the determination that the first set of consecutive video frames depicts identical content to the second set of consecutive video frames.

Abstract: Described herein are embodiments for joint adversarial training methods that incorporate both spatial transformation-based and pixel-value based attacks for improving image model robustness. Embodiments of a spatial transformation-based attack with an explicit notion of budgets are disclosed and embodiments of a practical methodology for efficient spatial attack generation are also disclosed. Furthermore, both pixel and spatial attacks are integrated into embodiments of a generation model and the complementary strengths of each other are leveraged for improving the overall model robustness. Extensive experimental results on several benchmark datasets compared with state-of-the-art methods verified the effectiveness of the presented method.

Abstract: This disclosure relates to a resource management system of a computing network. The design of the resource management system is an edge-Internet of Things (IoT) framework based on blockchain and smart contracts. The resource management system integrates a permissioned blockchain to link the edge cloud resources with each account for an IoT device, resource usage, and behavior of the IoT device. The resource management system uses credit-based resource management to control the resource of the IoT device that can be obtained from the edge server. Smart contracts are used to regulate behavior of the IoT device and enforce policies.

Abstract: Systems, methods, and non-transitory computer-readable media can receive a set of video frames associated with a video. A determination can be made that a threshold number of video frames of the set of video frames depict two or more reaction icons of a set of reaction icons. The video can be identified as a poll video based on the determining that the threshold number of video frames of the set of video frames depict two or more reaction icons of the set of reaction icons.

Abstract: Described herein are embodiments for a feature-scattering-based adversarial training approach for improving model robustness against adversarial attacks. Conventional adversarial training approaches leverage a supervised scheme, either targeted or non-targeted in generating attacks for training, which typically suffer from issues such as label leaking as noted in recent works. Embodiments of the disclosed approach generate adversarial images for training through feature scattering in the latent space, which is unsupervised in nature and avoids label leaking. More importantly, the presented approaches generate perturbed images in a collaborative fashion, taking the inter-sample relationships into consideration. Extensive experiments on different datasets compared with state-of-the-art approaches demonstrate the effectiveness of the presented embodiments.

Abstract: Described herein are embodiments for fast training of adversarially robust models against adversarial attacks. Embodiments for model training by perturbing both the image and the label, which may be referred to as Bilateral Adversarial Training (BAT), are disclosed. To generate the adversarial label, one or more closed-form heuristic solutions are derived. One-step targeted attack is used with the target label being the most confusing class. It is shown in various experiments that random start and the most confusing target attack effectively prevent the label leaking and gradient masking problem. Coupled with the adversarial label part, embodiments of presented models significantly improve the state-of-the-art results. Experiments on one or more computationally challenging dataset also demonstrate the effectiveness of the presented BAT method embodiments.

Abstract: A chloroquine gel and a preparation method and application thereof. A chloroquine nanosphere includes a water-soluble nanosphere carrier, and chloroquine or a chloroquine derivative, wherein a mass ratio of the chloroquine or the chloroquine derivative to the water-soluble nanosphere carrier is no more than 1:3.

Abstract: Described herein are embodiments for an approach to improve the robustness of an object detector against adversarial attacks. Existing attacks for object detectors and the impacts of individual task component on model robustness are systematically analyzed from a multi-task view of object detection. In one or more embodiments, a multi-task learning perspective of object detection is introduced and an asymmetric role of task losses is identified. One or more embodiments of an adversarial training method for robust object detection are presented to leverage the multiple sources of attacks for improving the robustness of object detection models.

Abstract: Described herein are embodiments for joint adversarial training methods that incorporate both spatial transformation-based and pixel-value based attacks for improving image model robustness. Embodiments of a spatial transformation-based attack with an explicit notion of budgets are disclosed and embodiments of a practical methodology for efficient spatial attack generation are also disclosed. Furthermore, both pixel and spatial attacks are integrated into embodiments of a generation model and the complementary strengths of each other are leveraged for improving the overall model robustness. Extensive experimental results on several benchmark datasets compared with state-of-the-art methods verified the effectiveness of the presented method.

Abstract: This disclosure relates to a resource management system of a computing network. The design of the resource management system is an edge-Internet of Things (IoT) framework based on blockchain and smart contracts. The resource management system integrates a permissioned blockchain to link the edge cloud resources with each account for an IoT device, resource usage, and behavior of the IoT device. The resource management system uses credit-based resource management to control the resource of the IoT device that can be obtained from the edge server. Smart contracts are used to regulate behavior of the IoT device and enforce policies.

Abstract: Provided are a method and an apparatus for adjusting image brightness. The method includes: acquiring a single-channel brightness image based on grayscales of each channel of an image to be processed; performing Gaussian filtering on the single-channel brightness image to acquire a Gaussian filtered image; adjusting grayscales of the Gaussian filtered image based on the grayscales of the Gaussian filtered image and a preset proportion; acquiring a grayscale change rate of each pixel with respect to the Gaussian filtered image after adjustment and the Gaussian filtered image before adjustment; and processing the image to be processed based on the grayscale change rate of each pixel to acquire a processed image.

Abstract: Provided are a method and an apparatus for adjusting image brightness. The method includes; acquiring a single-channel brightness image based on grayscales of each channel of an image to be processed; performing Gaussian filtering on the single-channel brightness image to acquire a Gaussian filtered image; adjusting grayscales of the Gaussian filtered image based on the grayscales of the Gaussian filtered image and a preset proportion; acquiring a grayscale change rate of each pixel with respect to the Gaussian filtered image after adjustment and the Gaussian filtered image before adjustment; and processing the image to be processed based on the grayscale change rate of each pixel to acquire a processed image.

Abstract: Provided are a method and an apparatus for adjusting image brightness. The method includes: acquiring an image to be processed, and acquiring a single-channel brightness image based on grayscales of each channel of the image to be processed; performing Gaussian filtering on the single-channel brightness image to acquire a Gaussian filtered image; adjusting grayscales of the Gaussian filtered image based on the grayscales of the Gaussian filtered image and a preset proportion; comparing the Gaussian filtered image after adjustment with the Gaussian filtered image before adjustment to acquire a grayscale change rate of each pixel through adjustment; processing the image to be processed based on the grayscale change rate of each pixel to acquire a processed image; and outputting the processed image.

Abstract: The disclosure relates to an image tone adjustment method, an apparatus thereof, and a computer-readable storage medium. The method includes: fitting overlapping areas to obtain a first correction parameter and a second correction parameter, performing interpolation on each pixel of the target image to obtain a corresponding correction parameter, correcting the target image according to the corresponding correction parameters; fitting an overlapping area between the corrected target image and the first image, and an overlapping area between the corrected target image and the second image to obtain a first RGB three-channel linear transformation parameter and a second RGB three-channel linear transformation parameter, performing linear interpolation on each pixel of the corrected target image to obtain a corresponding RGB three-channel linear transformation parameter, and transforming the corrected target image.

Abstract: A metal housing of an electronic communication device includes: a metal frame, and a plurality of micro holes. At least some of the plurality of micro holes penetrate the metal frame, with the plurality of micro holes being arranged into at least two rows to form a strip.

Abstract: A screw device, for mounting within a housing, configured to fixedly connect a first portion and a second portion of the housing, includes: a driving part fixedly connected within the first portion of the housing; a screw connected with the driving part; a nut fixedly connected within the second portion of the housing and matching the screw; and a chip configured to control the driving part to drive the screw to move relative to the nut, so as to achieve engagement or separation between the screw and the nut.

Abstract: Provided are a method and an apparatus for adjusting image brightness. The method includes: acquiring an image to be processed, and acquiring a single-channel brightness image based on grayscales of each channel of the image to be processed; performing Gaussian filtering on the single-channel brightness image to acquire a Gaussian filtered image; adjusting grayscales of the Gaussian filtered image based on the grayscales of the Gaussian filtered image and a preset proportion; comparing the Gaussian filtered image after adjustment with the Gaussian filtered image before adjustment to acquire a grayscale change rate of each pixel through adjustment; processing the image to be processed based on the grayscale change rate of each pixel to acquire a processed image; and outputting the processed image.

Abstract: The disclosure relates to an image tone adjustment method, an apparatus thereof, and a computer-readable storage medium. The method includes: fitting overlapping areas to obtain a first correction parameter and a second correction parameter, performing interpolation on each pixel of the target image to obtain a corresponding correction parameter, correcting the target image according to the corresponding correction parameters; fitting an overlapping area between the corrected target image and the first image, and an overlapping area between the corrected target image and the second image to obtain a first RGB three-channel linear transformation parameter and a second RGB three-channel linear transformation parameter, performing linear interpolation on each pixel of the corrected target image to obtain a corresponding RGB three-channel linear transformation parameter, and transforming the corrected target image.

Abstract: A method for panoramic image completion is disclosed. The method includes: acquiring a panoramic image; obtaining a projected image by mapping pixels of the panoramic image onto a polar coordinate system, wherein a long side component of the pixel coordinate of the pixels is corresponding to the polar angle of the polar coordinate system and a short side component of the pixel coordinate of the pixels is corresponding to a radial coordinate of the polar coordinate system; acquiring an incomplete region of the projected image, and obtaining a completed image by completing the incomplete region; and obtaining a completed panoramic image by inverse mapping the pixels of the completed image according to the polar coordinate system. Furthermore, a device for panoramic image completion is also disclosed. The above method and device for panoramic image completion take account of the perspective curvature of the panoramic image, to improve the degree of restoration.