Abstract: Systems, devices, and methods for fileless information management are provided. A system includes a content-management server in communication with at least one client device via a computer network. The client device has a client user interface to concurrently display a content-editing area and a content card-management area. The content-management server stores content data and content card template metadata shareable between client devices. The content-management server stores programming instructions for a content card-management program for receiving, storing, and transmitting content data via content card templates. A content card template may include metadata associating content data stored in the memory with a content binding in the content-editing area of a client device.

Abstract: An adjustable reading stand includes a base including two bottom plates foldable relative to each other, a resisting edge located on one side of the bottom plates and opposite limit grooves formed on the bottom plates, an butting member set on the bottom plates and providing an accommodating space near the resisting edge, and a positioning member having one side thereof set at the side of the accommodating space far from the resisting edge and an opposite side thereof provided with a retaining side that slides along the surface of the two bottom plates and embedded in any two opposite limit grooves. Thus, the inclination angle of the abutting member on the base can be adjusted, and the two bottom plates, the abutting member and the positioning member can be folded, so as to reduce the volume of the adjustable reading stand for convenient carrying.

Abstract: Techniques for providing redemptions via webpage unique asset tokens (UATs). A method incudes obtaining authenticity data from a user device when the user device has navigated to a webpage uniquely associated with a unique asset token (UAT), wherein the webpage is encoded into portions of code including code for providing the authenticity data; accessing a database with respect to the authenticity data in order to retrieve a redemption status of a premium, the database storing UAT nodes and premium nodes, wherein a first UAT node corresponds to the UAT and is connected to a first premium node, the first premium node representing the premium to be redeemed, wherein the portions of the code of the webpage are stored among the nodes; verifying eligibility of the premium based on the redemption status of the first premium node; and redeeming the premium when the eligibility of the premium has been verified.

Abstract: Techniques for tokenizing software applications (apps) and for providing application functionality using webpage non-fungible tokens (NFTs). A method includes encoding a webpage associated with apps into portions of code. Each portion of code causes tasks of the respective apps to be performed when executed by a device. The method also includes creating a database including at least webpage nodes and NFT nodes in which the encoded portions of code are stored among the nodes, one of the webpage nodes is associated with the webpage, one or more of the NFT nodes is connected to the webpage node that is associated with the webpage, and each of the NFT nodes corresponds to one of the apps. The method also includes providing the portions of code to a device authorized by an owner of the NFT node, thereby delivering the code for providing application functionality to the authorized device.

Abstract: A learning method for training an obfuscation network capable of obfuscating original data for privacy, including steps of: (a) inputting training data into the obfuscation network to filter frequency information of the training data and thus generate obfuscated data; and (b) (i) inputting the obfuscated data into a learning network to generate characteristic information by performing learning operation on the obfuscated data, (ii) generating at least one task loss by referring to (ii-1) the characteristic information and its corresponding ground truth or (ii-2) a task-specific output, generated by using the characteristic information, and its corresponding ground truth, and (iii) training at least one of the learning network and the obfuscation network through a backpropagation of the task loss.

Abstract: adjustable keyboard bracket mechanism includes fixed seat provided with first axle for use as pivot and rotatable second axle; bracket set including first support with one end thereof fastened to the first axle and the second axle and the other end thereof provided with third axle, second support having one end thereof fastened to the second axle and the other end thereof provided with fourth axle and adjustment device set fastened to the third axle and the fourth axle, the adjustment device set including rotating adjustment block having two opposite sides thereof respectively provided with first friction wheels and two mating friction blocks each having inner side thereof provided with second friction wheel for fitting the first friction wheel to form the positioning or rotating adjustment status of the adjustment device set; and connecting seat assembled on the rotating adjustment block opposite to the third axle and the fourth axle.

Abstract: A method for training a homography attention module (HAM) to perform multi-view object detection includes steps of: generating, from an i-th feature map corresponding to each of multiple training images representing multi-views of a target space, a 1-st to a d-th channel attention map for determining channel attention scores each channel included in the i-th feature map has for each of a 1-st to a d-th height plane of the target space, generating a 1-st to a d-th channel refined feature map by referring to channels with top k channel attention scores for each height, element-wisely multiplying them with corresponding spatial attention map generated therefrom to produce a 1-st to a d-th spatial refined feature map, and then homographically transforming them onto corresponding height plane and aggregating them to generate a BEV occupancy heatmap, which is used with its GT for training.

Abstract: A training image to be used in training a learning network is generated. The method of generating the training image includes steps of: (a) a labeling device, in response to acquiring an original image, (i) inputting the original image into an image recognition network to detect privacy-related regions from the original image, (ii) adding dummy regions, different from the detected privacy-related regions, onto the original image, and (iii) setting the privacy-related regions and the dummy regions as obfuscation-expected regions which represent regions to be obfuscated in the original image; (b) the labeling device generating an obfuscated image by obfuscating the obfuscation-expected regions; and (c) the labeling device labeling the obfuscated image to be corresponding to a task of the learning network to be trained, to thereby generate the training image to be used in training the learning network.

Abstract: A method for tracking one or more objects in a specific space is provided. The method includes steps of: (a) inputting original images of the specific space taken from camera to an obfuscation network and instructing the obfuscation network to obfuscate the original images to generate obfuscated images such that the obfuscated images are not identifiable as the original images by a human but the obfuscated images are identifiable as the original images by a learning network; (b) inputting the obfuscated images into the learning network, and instructing the learning network to detect obfuscated target objects, corresponding to target objects to be tracked, in the obfuscated images, to thereby output information on the obfuscated target objects; and (c) tracking the obfuscated target objects in the specific space by referring to the information on the obfuscated target objects.

Abstract: A method for training an obfuscation network is provided. The method includes steps of: a learning device (a) inputting training data into an obfuscation network to generate obfuscated data for training; (b) (i) inputting the obfuscated data for training into a discriminator to output a current obfuscation score for training and (ii) (ii-1) inputting first sub-data for training into a learning network to output first sub characteristic information for training and updating current updated learning parameters of the learning network to first sub updated learning parameters and (ii-2) while increasing an integer k from 2 to n, inputting k-th sub-data for training into the learning network to output k-th sub characteristic information for training and updating (k?1)-th sub updated learning parameters of the learning network to k-th sub updated learning parameters; and (c) updating previous updated obfuscation parameters of the obfuscation network to current updated obfuscation parameters.

Abstract: Methods for training a universal discriminator and an obfuscation network are provided.

Abstract: A smart charging method can supply a charging device with power, and the charging device includes charging regions disposed inside thereof, and each charging region includes sockets for charging mobile electronic devices. The charging device includes a control circuit to perform automatic charging control on the mobile electronic devices plugged into the charging regions. During process of the automatic charging control, inrush current is filtered out, and when the total current required by at least two charging regions is higher than a maximal current supply value, the at least two charging regions take turn to charge for a first charging period; otherwise, the charging regions satisfying the condition, and remaining charging region can take turn to charge for a second charging period until the mobile electronic devices are fully charged. The smart charging method can filter out inrush current and effectively increase the charging efficiency.

Abstract: A method for training an obfuscation network capable of performing distinct concealing processes for distinct regions of an original image is provided. The method includes steps of: a learning device (a) inputting a training image into the obfuscation network to generate an obfuscated training image by performing a 1-st to an n-th concealing process respectively on a 1-st to an n-th training region of the training image; (b) inputting the obfuscated training image into a 1-st to an n-th discriminator to respectively generate a 1-st to an n-th obfuscated image score on determining whether the obfuscated training image is real or fake, and inputting the obfuscated training image into an image recognition network to apply learning operation on the obfuscated training image to generate feature information for training; and (c) training the obfuscation network such that an accumulated loss is maximized, and an accuracy loss is minimized.

Abstract: A method for preventing breach of original data for deep learning is provided. The method includes steps of: a data breach preventing device (a) adding noise onto the acquired original data to generate 1-st noisy data; and (b)(b1) while increasing an integer k from 1 to an integer larger than 0, (i) inputting k-th noisy data into a learning network, to apply learning operations to the k-th noisy data using learned parameters of the learning network, and to output k-th characteristic information, and (ii) launching an adversarial attack on the k-th noisy data via backpropagation using at least one of (ii-1) (k_1)-st losses calculated using the k-th characteristic information and a 1-st ground truth, and (ii-2) (k_2)-nd losses calculated using (1) a k-th task specific output and (2) a 2-nd ground truth, and generating (k+1)-th noisy data, and (b2) as a result, generating n-th noisy data as watermarked data.

Abstract: A method for concealing original data to protect personal information is provided. The method includes steps of: a data obfuscation device (a) if the original data is acquired, inputting the original data or its modified data into a learning network, and allowing the learning network to (i) apply a network operation to the original data or the modified data using learned parameters of the learning network and thus to (ii) output characteristic information on the original data or the modified data; and (b) updating the original data or the modified data via backpropagation using part of (i) 1-st losses calculated by referring to the characteristic information and its corresponding 1-st ground truth, and (ii) 2-nd losses calculated by referring to (ii-1) a task specific output generated by using the characteristic information and (ii-2) a 2-nd ground truth corresponding to the task specific output, to thereby generate obfuscated data.

Abstract: An accommodation cabinet with a handtoollessly assembled frame body is disclosed. The accommodation cabinet includes a cabinet body and the frame body. The cabinet body includes a spring clip disposed on a side thereof, and a button protruded on a surface of the spring clip, and a fastening part disposed on the spring clip and around the button. The frame body is mounted on a side of the cabinet body, and the frame body includes a groove configured for insertion of the button, and an engaging part for combining with the fastening part. When a user wants to change the frame body of the cabinet body, the user can press the button to elastically deform the spring clip, so as to disengage the fastening part from the engaging part, and then take out the frame body and assemble different color of other frame body with the cabinet body.

Abstract: An adjustable lifting support device is disclosed. The adjustable lifting support device includes a base, a lifting rod and an adjusting device. The base includes a placing plate and a connecting part disposed on the placing plate. The lifting rod includes a rod disposed on the connecting part and including teeth arranged longitudinally. The adjusting device includes a connecting holder mounted on the rod, and the bottom of the connecting holder is pivotally connected to an engaging member which is jointed with a linking device. The placing plate can be placed on desktop and a support plate disposed on the connecting part can be adjusted in position. The linking device can be used to drive the engaging member to engage with or separate from the teeth of the lifting rod, so that the connecting holder can be slidably lifted and locked on the rod of the lifting rod.

Abstract: A method for learning a data embedding network is provided. The method includes steps of: a learning device acquiring and inputting original training data and mark training data into the data embedding network which integrates them and generates marked training data; inputting the marked training data into a learning network which applies a network operation to them and generates 1-st characteristic information, and inputting the original training data into the learning network which applies a network operation to them and generates 2-nd characteristic information; learning the data embedding network such that a data error is minimized, by referring to part of errors referring to the 1-st and the 2-nd characteristic information and errors referring to task specific outputs and their ground truths, and a marked data score is maximized, and learning a discriminator such that a original data score is maximized and the marked data score is minimized.