Abstract: Provided are an interaction method and apparatus in a live streaming room, and a device and a storage medium. The method comprises: in response to a trigger operation for a preset duet entry on a live streaming room page, jumping from the live streaming room page to a camera capture page, wherein a host portrait cutout corresponding to the live streaming room page is displayed on the camera capture page and a capture control is provided on the camera capture page acquiring multimedia information captured by a camera, in response to a trigger operation for the capture control on the camera capture page, wherein the multimedia information comprises an image or a video; and synthesizing the multimedia information with the host portrait cutout to obtain a duet multimedia product.

Abstract: A photographing apparatus and an inspection device are provided. The photographing apparatus comprises a camera, a light supplementing structure, and a closed housing; the camera is provided with a lens, and positioned in and connected to the housing; the housing is provided with at least one photographing portion configured to be attached to an object to be photographed, and a space is formed between the lens and the photographing portion; the light supplementing structure is positioned in and connected to the housing, the light supplementing structure is configured to emit light to said object through the space and the photographing portion, and the lens is configured to capture an image of said object via the photographing portion. The inspection device comprises a rack and the photographing apparatus, and the housing is connected to the rack.

Abstract: Provided are an interaction method and apparatus in a live streaming room, a device, and a storage medium. The method comprises: in response to a trigger operation for a preset drawing entry on a live streaming room page, jumping to a graphic drawing page from the live streaming room page, a drawing trajectory set for a preset object being displayed on the graphical drawing page; when a drawing stroke on the graphic drawing page is received, matching the drawing stroke with the drawing trajectory; and if it is determined that the drawing stroke is successfully matched with the drawing trajectory, displaying prompt information about successful participation in a preset activity, the preset activity and the preset drawing entry having a correspondence.

Abstract: The present disclosure provides a superhydrophobic and self-cleaning anticoagulant composite coating material and a preparation method and use thereof, and relates to the technical field of biomedical materials. In the coating material provided by the present disclosure, a titanium dioxide nanotube-based structure increases microscopic roughness of a surface of a titanium-based metal substrate, and a hydrophobic modification layer reduces surface energy of the material. The rough structure and the hydrophobic modification layer have a synergistic effect to construct a superhydrophobic surface, making the surface of the material have self-cleaning characteristics and low adhesion. Air can be retained on the surface of the material to form an air layer, thereby reducing the contact area between the material and bacteria and platelets in the blood, and inhibiting adhesion of the bacteria, platelets, and plasma proteins to the material.

Abstract: Disclosed are a preparation method for manganese-doped red phosphor, a device and a backlight module including the product. The method includes: 1) mixing A2BF6 polycrystalline particles with mill balls; 2) mixing A2BF6 powder obtained after ball-milling with a hydrofluoric acid for secondary crystallization; 3) filtering out solid particles in A2BF6 and hydrofluoric acid solution after the secondary crystallization; 4) performing ion exchange between A2BF6 particles and A2BF6; and 5) filtering out solid particles to obtain a filter cake, and performing drying treatment to obtain manganese-doped red phosphor.

Abstract: Using a deep learning inference system, respective similarities are measured for each of a set of intermediate representations to input information used as an input to the deep learning inference system. The deep learning inference system includes multiple layers, each layer producing one or more associated intermediate representations. Selection is made of a subset of the set of intermediate representations that are most similar to the input information. Using the selected subset of intermediate representations, a partitioning point is determined in the multiple layers used to partition the multiple layers into two partitions defined so that information leakage for the two partitions will meet a privacy parameter when a first of the two partitions is prevented from leaking information. The partitioning point is output for use in partitioning the multiple layers of the deep learning inference system into the two partitions.

Abstract: Decoy data is generated from regular data. A deep neural network, which has been trained with the regular data, is trained with the decoy data. The trained deep neural network, responsive to a client request comprising input data, is operated on the input data. Post-processing is performed using at least an output of the operated trained deep neural network to determine whether the input data is regular data or decoy data. One or more actions are performed based on a result of the performed post-processing.

Abstract: This application provides a transmission absorbing structure and an antenna in-band characteristics test system, relating to design of microwave antennas for radar and communication systems. The transmission absorbing structure includes a coupling feed structure provided with coupling slots for energy coupling with a to-be-tested antenna, two equivalent electric wall structures parallel to each other, and two equivalent magnetic wall structures parallel to each other. The two equivalent electric wall structures and the two equivalent magnetic wall structures together enclose the coupling feed structure, and form a transverse electromagnetic mode (TEM) waveguide. The system includes a vector network analyzer, a to-be-tested antenna electrically connected to the vector network analyzer, and a transmission absorbing structure.

Abstract: Deep learning training service framework mechanisms are provided. The mechanisms receive encrypted training datasets for training a deep learning model, execute a FrontNet subnet model of the deep learning model in a trusted execution environment, and execute a BackNet subnet model of the deep learning model external to the trusted execution environment. The mechanisms decrypt, within the trusted execution environment, the encrypted training datasets and train the FrontNet subnet model and BackNet subnet model of the deep learning model based on the decrypted training datasets. The FrontNet subnet model is trained within the trusted execution environment and provides intermediate representations to the BackNet subnet model which is trained external to the trusted execution environment using the intermediate representations. The mechanisms release a trained deep learning model comprising a trained FrontNet subnet model and a trained BackNet subnet model, to the one or more client computing devices.

Abstract: This application provides a transmission absorbing structure and an antenna in-band characteristics test system, relating to design of microwave antennas for radar and communication systems. The transmission absorbing structure includes a coupling feed structure provided with coupling slots for energy coupling with a to-be-tested antenna, two equivalent electric wall structures parallel to each other, and two equivalent magnetic wall structures parallel to each other. The two equivalent electric wall structures and the two equivalent magnetic wall structures together enclose the coupling feed structure, and form a transverse electromagnetic mode (TEM) waveguide. The system includes a vector network analyzer, a to-be-tested antenna electrically connected to the vector network analyzer, and a transmission absorbing structure.

Abstract: Mechanisms are provided for detecting abnormal system call sequences in a monitored computing environment. The mechanisms receive, from a computing system resource of the monitored computing environment, a system call of an observed system call sequence for evaluation. A trained recurrent neural network (RNN), trained to predict system call sequences, processes the system call to generate a prediction of a subsequent system call in a predicted system call sequence. Abnormal call sequence logic compares the subsequent system call in the predicted system call sequence to an observed system call in the observed system call sequence and identifies a difference between the predicted system call sequence and the observed system call sequence based on results of the comparing. The abnormal call sequence logic generates an alert notification in response to identifying the difference.

Abstract: Malware is detected and mitigated by differentiating HTTP error generation patterns between errors generated by malware, and errors generated by benign users/software. In one embodiment, a malware detector system receives traffic that includes HTTP errors and successful HTTP requests. Error traffic and the successful request traffic are segmented for further analysis. The error traffic is supplied to a clustering component, which groups the errors, e.g., based on their URI pages and parameters. During clustering, various statistical features are extracted (as feature vectors) from one or more perspectives, namely, error provenance, error generation, and error recovery. The feature vectors are supplied to a classifier component, which is trained to distinguish malware-generated errors from benign errors. Once trained, the classifier takes an error cluster and its surrounding successful HTTP requests as inputs, and it produces a verdict on whether a particular cluster is malicious.

Abstract: Decoy data is generated from regular data. A deep neural network, which has been trained with the regular data, is trained with the decoy data. The trained deep neural network, responsive to a client request comprising input data, is operated on the input data. Post-processing is performed using at least an output of the operated trained deep neural network to determine whether the input data is regular data or decoy data. One or more actions are performed based on a result of the performed post-processing.

Abstract: Decoy data is generated from regular data. A deep neural network, which has been trained with the regular data, is trained with the decoy data. The trained deep neural network, responsive to a client request comprising input data, is operated on the input data. Post-processing is performed using at least an output of the operated trained deep neural network to determine whether the input data is regular data or decoy data. One or more actions are performed based on a result of the performed post-processing.

Abstract: Mechanisms are provided to implement an enhanced privacy deep learning system framework (hereafter “framework”). The framework receives, from a client computing device, an encrypted first subnet model of a neural network, where the first subnet model is one partition of multiple partitions of the neural network. The framework loads the encrypted first subnet model into a trusted execution environment (TEE) of the framework, decrypts the first subnet model, within the TEE, and executes the first subnet model within the TEE. The framework receives encrypted input data from the client computing device, loads the encrypted input data into the TEE, decrypts the input data, and processes the input data in the TEE using the first subnet model executing within the TEE.

Abstract: Mechanisms are provided to implement an enhanced privacy deep learning system framework (hereafter “framework”). The framework receives, from a client computing device, an encrypted first subnet model of a neural network, where the first subnet model is one partition of multiple partitions of the neural network. The framework loads the encrypted first subnet model into a trusted execution environment (TEE) of the framework, decrypts the first subnet model, within the TEE, and executes the first subnet model within the TEE. The framework receives encrypted input data from the client computing device, loads the encrypted input data into the TEE, decrypts the input data, and processes the input data in the TEE using the first subnet model executing within the TEE.

Abstract: Mechanisms are provided for detecting abnormal system call sequences in a monitored computing environment. The mechanisms receive, from a computing system resource of the monitored computing environment, a system call of an observed system call sequence for evaluation. A trained recurrent neural network (RNN), trained to predict system call sequences, processes the system call to generate a prediction of a subsequent system call in a predicted system call sequence. Abnormal call sequence logic compares the subsequent system call in the predicted system call sequence to an observed system call in the observed system call sequence and identifies a difference between the predicted system call sequence and the observed system call sequence based on results of the comparing. The abnormal call sequence logic generates an alert notification in response to identifying the difference.

Abstract: A method, apparatus and computer program product to protect a deep neural network (DNN) having a plurality of layers including one or more intermediate layers. In this approach, a training data set is received. During training of the DNN using the received training data set, a representation of activations associated with an intermediate layer is recorded. For at least one or more of the representations, a separate classifier (model) is trained. The classifiers, collectively, are used to train an outlier detection model. Following training, the outliner detection model is used to detect an adversarial input on the deep neural network. The outlier detection model generates a prediction, and an indicator whether a given input is the adversarial input. According to a further aspect, an action is taken to protect a deployed system associated with the DNN in response to detection of the adversary input.

Abstract: Malware is detected and mitigated by differentiating HTTP error generation patterns between errors generated by malware, and errors generated by benign users/software. In one embodiment, a malware detector system receives traffic that includes HTTP errors and successful HTTP requests. Error traffic and the successful request traffic are segmented for further analysis. The error traffic is supplied to a clustering component, which groups the errors, e.g., based on their URI pages and parameters. During clustering, various statistical features are extracted (as feature vectors) from one or more perspectives, namely, error provenance, error generation, and error recovery. The feature vectors are supplied to a classifier component, which is trained to distinguish malware-generated errors from benign errors. Once trained, the classifier takes an error cluster and its surrounding successful HTTP requests as inputs, and it produces a verdict on whether a particular cluster is malicious.

Abstract: There are provided a network identity authentication method, a network identity authentication system, a user agent device used in the network identity authentication method and the network identity authentication system, and a computer-readable storage medium. The network identity authentication method includes: acquiring, by a user agent, identity information and a registration rule of a target website via a network terminal; acquiring registration information for the target website based on the identity information or generating registration information for the target website according to the registration rule; transmitting the identity information and the registration information to a server agent and sending, by the server agent based on the identity information and the registration information, an authentication request to a website server to complete an authentication process.