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As violent criminals, such as child sex offenders, tend to have high recidivism rates in modern society, there is a need to prevent such offenders from approaching socially disadvantaged and crime-prone areas, such as schools or childcare centers. Accordingly, national governments and related institutions have installed surveillance cameras and provided additional personnel to manage and monitor them via video surveillance equipment. However, naked-eye monitoring by guards and manual image processing cannot properly evaluate the video captured by surveillance cameras. To address the various problems of conventional systems that simply store and retrieve image data, a system is needed that can actively classify captured images in real-time, in addition to assisting surveillance personnel. Therefore, this paper proposes a video surveillance system based on a composable deep face recognition method. The proposed system detects the faces of criminals in real time from videos captured by a surveillance camera and notifies relevant institutions of the appearance of criminals. For real-time face detection, a down-sampled image forked from the original is used to localize unspecified faces. To improve accuracy and confidence in the recognition task, a scoring method based on face tracking is proposed. The final score combines the recognition confidence and the standard score to determine the embedding distance from the criminal face embedding data. The blind spots of surveillance personnel can be effectively addressed through early detection of criminals approaching crime-prone areas. The contributions of the paper are as follows. The proposed system can process images from surveillance cameras in real-time by using down-sampling. It can effectively identify the identity of criminals by using a face tracking ID unit and minimizes prediction reversal by solving the congested embedding problem in the feature space that may occur when performing identification matching on a large amount of face embedding DBs. Additionally, the reliability of the identification results is complemented by an identification score accumulation method. In this paper, we prototyped the proposed system and experimented with the recognition model, achieving an accuracy of 0.900 and an F-1 score of 0.943. We also experimentally confirmed that the models proposed in other studies have higher performance when using the tracked instance-level face identification method proposed in this paper. It is expected that the proposed system can be used to locate criminals and protect national facilities, and such responses can quickly prevent accidents/incidents. The dataset and code are available at https://github.com/aengoo/focusface

Human pose estimation has long been researched as a significant topic in computer vision. However, studies via deep learning models are insufficient because of the lack of 2D and 3D skeleton data in various domains. An augmentation technique is applied to solve the problem of data scarcity. Data augmentation techniques can improve the performance of an analytical model by increasing the amount of data. However, the model’s performance is degraded if the augmented results differ significantly from the actual distribution. Therefore, it is necessary to implement an optimized augmentation policy for image datasets. This study proposed a dimensional expansion and time-series data augmentation policy for pose estimation based on skeletons. The proposed method improves the model’s performance by using 3D skeleton data. The 3D skeleton data were preprocessed through an affine transformation. The data were augmented for dimensional expansion from 2D to 3D data. In addition, sampling was applied to the data in consideration of time-series features, and thus, the number of frames per unit time was redefined. Subsequently, part of the information was lost by cutout, and thus, the data size, rather than the data shape, was changed. Through the image and video augmentation policies and cutout expansion, search candidates for 3D time-series data augmentation policies were extracted from the number of cases generated through the combination of 16 skeleton augmentation policies, 11 probabilities, and ten intensities. Finally, 20 candidates were extracted, and the five best-performing policies were applied.

To use artificial intelligence to assist in diagnoses applications, a model to utilize quality data is required, which results in massive time and cost. In medical data, data imbalance occurs because the amount of data with lesions is less than that without lesions. To overcome this limitation, this study proposes a progressive growth of generative adversarial networks (PGGAN)-based anomaly classification on chest X-rays using weighted multi-scale similarity. An anomaly detection method is applied to learn the distribution of normal images to solve the problem of data imbalance. The use of PGGAN, which is a model that generates high-resolution images by gradually adding layers, enables to find image characteristics on a multi-scale and define the similarity between an original image and a generated image. The anomaly score is calculated by applying the weighted arithmetic mean to a resolution-by-resolution similarity. The threshold is defined after the analysis of the F1-score, and then the classification performance is evaluated. The accuracy of the proposed model was assessed using a confusion matrix and compared with that of a conventional classification model, and the efficiency was demonstrated through ablation studies. The classification accuracy of the test dataset was 0.8525. Compared to a U-net-based disease classifier with low-resolution which accuracy was 0.8410, the performance of the proposed model was 0.8507, exhibited an improvement.

Silymarin, a standardized extract from milk thistle fruits has been found to exhibit anti-cancer effects against various cancers. Here, we explored the anti-cancer activity of silymarin and its molecular target in human oral cancer in vitro and in vivo. Silymarin dose-dependently inhibited the proliferation of HSC-4 oral cancer cells and promoted caspase-dependent apoptosis. A human apoptosis protein array kit showed that death receptor 5 may be involved in silymarin-induced apoptosis, which was also shown through western blotting, immunocytochemistry, and reverse transcription-polymerase chain reaction. Silymarin increased cleaved caspase-8 and truncated Bid, leading to accumulation of cytochrome c. In addition, silymarin activated death receptor 5/caspase-8 to induce apoptotic cell death in two other oral cancer cell lines (YD15 and Ca9.22). Silymarin also suppressed tumor growth and volume without any hepatic or renal toxicity in vivo. Taken together, these results provide in vitro and in vivo evidence supporting the anti-cancer effect of silymarin and death receptor 5, and caspase-8 may be essential players in silymarin-mediated apoptosis in oral cancer.

Mostly, artificial intelligence does not show any definite change in emotions. For this reason, it is hard to demonstrate empathy in communication with humans. If frequency modification is applied to neutral emotions, or if a different emotional frequency is added to them, it is possible to develop artificial intelligence with emotions. This study proposes the emotion conversion using the Generative Adversarial Network (GAN) based voice frequency synthesis. The proposed method extracts a frequency from speech data of twenty-four actors and actresses. In other words, it extracts voice features of their different emotions, preserves linguistic features, and converts emotions only. After that, it generates a frequency in variational auto-encoding Wasserstein generative adversarial network (VAW-GAN) in order to make prosody and preserve linguistic information. That makes it possible to learn speech features in parallel. Finally, it corrects a frequency by employing Amplitude Scaling. With the use of the spectral conversion of logarithmic scale, it is converted into a frequency in consideration of human hearing features. Accordingly, the proposed technique provides the emotion conversion of speeches in order to express emotions in line with artificially generated voices or speeches. [ABSTRACT FROM AUTHOR]