Abstract: The present invention relates to a composition that can suppress angiogenesis and overcome the resistance of cancer cells occurring in a low-carbohydrate state, and thus can be used for angiogenesis suppression and adjuvant anticancer treatment.

Abstract: Provided is an artificial neural network learning apparatus for deep learning. The apparatus includes an input unit configured to acquire an input data or a training data, a memory configured to store the input data, the training data, and a deep learning artificial neural network model, and a processor configured to perform computation based on the artificial neural network model, in which the processor sets the initial weight depending on the number of nodes belonging to a first layer and the number of nodes belonging to a second layer of the artificial neural network model, and determines the initial weight by compensation by multiplying a standard deviation (?) by a square root of a reciprocal of a probability of a normal probability distribution for a remaining section except for a section in which an output value of the activation function converges to a specific value.

Abstract: Disclosed a method for augmenting training data by combining an object and a background with each other. The method includes extracting an object image, wherein the object image is a machine learning target; determining a type of the object image; receiving a background image, wherein the background image comprises a plurality of different background regions; identifying a first background region and a second background region among the plurality of different background regions; and combining the object image with the first background region and the second background region to augment training data, wherein combining the object image with the first background region and the second background region includes randomly positioning an image of a first type object corresponding to the first background region into the first background region, and randomly positioning an image of a second type object corresponding to the second background region into the second background region.

Abstract: Provided is a method for reconstructing content image data. The method includes selecting a first point and a second point in a first image of first content, selecting a third point and a fourth point in a second image of second content (the second image is an image corresponding to the first image and the third point and the fourth point are points in an image corresponding to the first point and the second point, respectively), generating a first reference vector using the first point and the second point, generating a second reference vector using the third point and the fourth point, calculating a rotation, scale, and transformation (RST) value from the first image to the second image using the first reference vector and the second reference vector; and reconstructing the second content using the calculated RST value.

Abstract: Provided is an artificial neural network learning apparatus for deep learning. The apparatus includes an input unit configured to acquire an input data or a training data, a memory configured to store the input data, the training data, and a deep learning artificial neural network model, and a processor configured to perform computation based on the artificial neural network model, in which the processor sets the initial weight depending on the number of nodes belonging to a first layer and the number of nodes belonging to a second layer of the artificial neural network model, and determines the initial weight by compensation by multiplying a standard deviation (?) by a square root of a reciprocal of a probability of a normal probability distribution for a remaining section except for a section in which an output value of the activation function converges to a specific value.

Abstract: One aspect of the present invention discloses an apparatus for detecting additional data for preventing forgery of video data and displaying the video content. The apparatus includes a content input unit configured to receive the video content (the video content including the additional data), a display unit configured to display the video content, an additional data detector configured to detect the additional data from the video content (the additional data including a watermark), and a display controller configured to control a display screen of the video content according to an additional data detection result.

Abstract: A method and apparatus for watermarking digital content and a data extraction method are disclosed. The digital content watermarking method includes: generating private and public keys corresponding to the user terminal; generating watermark data containing user identity information corresponding to the user terminal and user details associated with the watermark data; generating encrypted data by encrypting the watermark data and the user details with the private key; transmitting the watermark data, the encrypted data, and the public key to a server; receiving digital content when authorized by the server; and watermarking the digital content with the watermark data.

Abstract: A method of inserting additional data into a moving image stream, according to some embodiments of the present invention, includes receiving a first coded bitstream, electing a first picture PA into which additional data is to be inserted from the first coded bitstream, generating a first picture PA? by inserting the additional data into the selected first picture PA, selecting a second picture PB directly referring to the first picture PA, generating a second picture PB? by transforming and coding the selected second picture PB in a manner that decoding the second picture PB? obtains the second picture PB with reference to the first picture PA? and generating a data sequence based on the first picture PA? and the second picture PB.

Abstract: Disclosed is a method for executing an activation function for a deep learning algorithm. The method includes: determining whether an input value to a first node of an artificial neural network related to the deep learning algorithm is positive or negative; executing a first activation function in response to the input value being positive, or executing a second activation function in response to the input value being negative; and providing a value resulted from the execution of the first activation function or the second activation value to a second node of the artificial neural network, wherein the first activation function is a Rectified Linear Unit (ReLU) function, wherein the second activation function is a linear function having a first gradient in a first section of a negative number region and a second gradient in a second section of the negative number region.

Abstract: One aspect of the present invention discloses an apparatus for detecting additional data for preventing forgery of video data and displaying the video content. The apparatus includes a content input unit configured to receive the video content (the video content including the additional data), a display unit configured to display the video content, an additional data detector configured to detect the additional data from the video content (the additional data including a watermark), and a display controller configured to control a display screen of the video content according to an additional data detection result.

Abstract: Provided is a method for reconstructing content image data. The method includes selecting a first point and a second point in a first image of first content, selecting a third point and a fourth point in a second image of second content (the second image is an image corresponding to the first image and the third point and the fourth point are points in an image corresponding to the first point and the second point, respectively), generating a first reference vector using the first point and the second point, generating a second reference vector using the third point and the fourth point, calculating a rotation, scale, and transformation (RST) value from the first image to the second image using the first reference vector and the second reference vector; and reconstructing the second content using the calculated RST value.

Abstract: Disclosed is a watermark embedding method. The method includes receiving an original image, generating a watermark image based on the selected watermark pattern, generating an alpha blended stego image by alpha blending the original image and the watermark image, and providing the alpha blended stego image, in which the watermark image is generated by considering at least one of allowable pixel value range and alpha value ? range of the watermark pattern applied to a first pixel of the original image, wherein the at least one of allowable pixel value range and alpha value ? range are determined by the difference of pixel value of the first pixel between the original image and the stego image by embedding the watermark pattern to the first pixel.

Abstract: A method and apparatus for watermarking digital content and a data extraction method are disclosed. The digital content watermarking method includes: generating private and public keys corresponding to the user terminal; generating watermark data containing user identity information corresponding to the user terminal and user details associated with the watermark data; generating encrypted data by encrypting the watermark data and the user details with the private key; transmitting the watermark data, the encrypted data, and the public key to a server; receiving digital content when authorized by the server; and watermarking the digital content with the watermark data.

Abstract: One aspect of the present invention discloses a watermark embedding method in a watermark embedding apparatus. The method includes inputting a video frame, generating at least two tiles by spatially dividing the video frame, and embedding watermark information to each of the at least two tiles.

Abstract: One aspect of the present invention discloses a watermark embedding method. The method includes: inputting an original bitstream; determining a first frame for embedding a watermark in the input original bitstream; selecting one of frames after the first frame as a second frame; generating the first frame a bidirectional-coded frame (B frame) referring to the second frame; generating the second frame as a reference frame of the first frame; and embedding the watermark in the first frame generated as the B frame.

Abstract: One aspect of the present invention discloses a watermark embedding method. The method includes: inputting an original bitstream; determining a first frame for embedding a watermark in the input original bitstream; selecting one of frames after the first frame as a second frame; generating the first frame a bidirectional-coded frame (B frame) referring to the second frame; generating the second frame as a reference frame of the first frame; and embedding the watermark in the first frame generated as the B frame.

Abstract: Disclosed is a watermark embedding method. The method includes receiving an original image, generating a watermark image based on the selected watermark pattern, generating an alpha blended stego image by alpha blending the original image and the watermark image, and providing the alpha blended stego image, in which the watermark image is generated by considering at least one of allowable pixel value range and alpha value ? range of the watermark pattern applied to a first pixel of the original image, wherein the at least one of allowable pixel value range and alpha value ? range are determined by the difference of pixel value of the first pixel between the original image and the stego image by embedding the watermark pattern to the first pixel.

Abstract: Provided are an apparatus and a method for detecting a scene cut frame. The apparatus and a method for detecting a scene cut frame includes an image selector configured to select input images by n image frames while shifting an input image at h frame intervals; a candidate frame detector configured to detect a candidate frame from the selected n image frames using at least one of a plurality of codecs and a plurality of scene cut detecting algorithms; and a final frame detector configured to detect a frame having a count value higher than a threshold value as a final scene cut frame by performing histogram analysis based on the detected candidate frame, wherein the h and the n are a real number greater than zero and the h is smaller than the n.

Abstract: Provided are an apparatus and a method for detecting a scene cut frame. The apparatus and a method for detecting a scene cut frame includes an image selector configured to select input images by n image frames while shifting an input image at h frame intervals; a candidate frame detector configured to detect a candidate frame from the selected n image frames using at least one of a plurality of codecs and a plurality of scene cut detecting algorithms; and a final frame detector configured to detect a frame having a count value higher than a threshold value as a final scene cut frame by performing histogram analysis based on the detected candidate frame, wherein the h and the n are a real number greater than zero and the h is smaller than the n.

Abstract: A method of inserting additional data into a moving image stream, according to some embodiments of the present invention, includes receiving a first coded bitstream, electing a first picture PA into which additional data is to be inserted from the first coded bitstream, generating a first picture PA? by inserting the additional data into the selected first picture PA, selecting a second picture PB directly referring to the first picture PA, generating a second picture PB? by transforming and coding the selected second picture PB in a manner that decoding the second picture PB? obtains the second picture PB with reference to the first picture PA? and generating a data sequence based on the first picture PA? and the second picture PB.