Nowadays, with the vigorous development of self-media services, more and more individual users upload videos freely. While bringing goodness, it also inevitably brings evil. Therefore, it is particularly necessary to identify and supervise illegal videos through network stream. However, many video streaming services, such as YouTube, have applied encryption to protect users’ privacy, which makes it more difficult to analyze network stream. Many researches show that DASH (Dynamic Adaptive Streaming over HTTP) will leak information about video segmentation, which is related to the video content. Consequently, it is possible to analyze the content of encrypted video stream without decryption. Previous studies have proposed a series of encrypted video identification methods based on this. However, most of them need to wait for a long video playback time, such as more than 10s, or even wait for the entire video playback to complete the identification. In this paper, we propose a fast, lightweight, and accurate method named Long-Short Terms Frequency(LSTF) for online encrypted video identification. Experiments have proved that compared with the state-of-the-art, our method has advantages in both speed and accuracy, and even if CDN switching occurs during the video playback, it still has a high identification accuracy.

As the scale of data on the Internet continues to increase, the management and monitoring of time series data are facing significant challenges. Efficient and stable time-series data anomaly detection methods are necessary for fields such as traffic detection, power grid operation and maintenance, financial stock market, and industry. However, there are fewer abnormal data labels in time series data, and the labeling cost is high. Traditional expert knowledge-based supervised methods have been difficult to adapt to large-scale data metric management and timely abnormal alarms. At the same time, the way based on the new neural network has an extensive time overhead when faced with massive data, and it isn’t easy to apply it in a real-time industrial environment. Therefore, we propose the TSFN model in this paper, an unsupervised method of a transformer-based self-feedback network. Which can capture timing dependencies, learn normal data distribution and improve the self-feedback ability for sensitive areas, and can be used to detect anomalies in multidimensional time series more quickly. Our experimental research on five public datasets shows that our method has fast training speed, good stability, excellent anomaly detection ability, and good generalization ability compared with the baseline method.

With the increasing capacity of wireless networks, more people would like to consume the 360-degree panoramic video (PV) in virtual reality (VR) applications as its immersive experience. However, due to the super-high resolution of the PV and the dynamic features of wireless networks, it is very difficult to efficiently deliver PVs over wireless networks. The traditionally independent PV encoding and networking sometimes also results in the PV quality deterioration since it neglects the harmony between the source encoding and networking. In this paper, a joint source encoding and networking optimization scheme is proposed to transmit the PV over LTE-A downlink. The PV encoding parameters during the source compression, the modulation and coding scheme (MCS), and relay selection during the networking are jointly considered to optimize the end-to-end PV quality. In addition, the video quality for region of interest (RoI, the possible viewport region) is enhanced by allowing a larger latency bound in the joint source encoding and networking optimization. Experimental results show that the proposed scheme achieves significant performance improvement for the quality of the received PV over traditional PV streaming approaches.

Mobile communication data analysis has been often used as a background application to motivate many data mining problems. However, very few data mining researchers have a chance to see a working data mining system on real mobile communication data. In this demo, we showcase our new system MobileMiner on a real mobile communication data set, which presents a case study of business solutions using state-of-the-art data mining techniques. MobileMiner adaptively profiles users' behavior from their calling and moving record streams. Customer segmentation and social community analysis can be conducted based on user profiles. We show how data mining techniques can help in mobile communication data analysis. Moreover, we also show some interesting observations which still cannot be mined by the current techniques, and thus may motivate new research and development.