Active Authentication via Hiding Programs in Digital Contents

We propose a generic active authentication framework via hiding programs in digital contents, especially designed for H.264 / MPEG-4 AVC video formats. Besides using cryptography and steganography techniques, we bind a scripting language runtime as process virtual machine, giving the developer the possibility to design their own variant from passive authentication to active code execution

Hierarchical triple mergers: testing Hawking's area theorem with the inspiral signals

Hawking's area theorem is one of the fundamental laws of black holes (BHs), which has been tested at a confidence level of $\sim 95\%$ with gravitational wave (GW) observations by analyzing the inspiral and ringdown portions of GW signals independently. In this work, we propose to carry out the test in a new way with the hierarchical triple merger (i.e., two successive BH mergers occurred sequentially within the observation window of GW detectors), for which the properties of the progenitor BHs and the remnant BH of the first coalescence can be reliably inferred from the inspiral portions of the two mergers. As revealed in our simulation, a test of the BH area law can be achieved at the significance level of $\gtrsim 3\sigma$ for the hierarchical triple merger events detected in LIGO/Virgo/KAGRA's O4/O5 runs. If the hierarchical triple mergers contribute a $\gtrsim 0.1\%$ fraction to the detected BBHs, a precision test of the BH area law with such systems is achievable in the near future. Our method also provides an additional criterion to establish the hierarchical triple merger origin of some candidate events.Comment: 5 pages, 5 figures, 1 tabl

Modeling Paying Behavior in Game Social Networks

Online gaming is one of the largest industries on the Internet, generating tens of billions of dollars in revenues annually. One core problem in online game is to find and convert free users into paying customers, which is of great importance for the sustainable development of almost all online games. Although much research has been conducted, there are still several challenges that remain largely unsolved: What are the fundamental factors that trigger the users to pay? How does users? paying behavior influence each other in the game social network? How to design a prediction model to recognize those potential users who are likely to pay? In this paper, employing two large online games as the basis, we study how a user becomes a new paying user in the games. In particular, we examine how users' paying behavior influences each other in the game social network. We study this problem from various sociological perspectives including strong/weak ties, social structural diversity and social influence. Based on the discovered patterns, we propose a learning framework to predict potential new payers. The framework can learn a model using features associated with users and then use the social relationships between users to refine the learned model. We test the proposed framework using nearly 50 billion user activities from two real games. Our experiments show that the proposed framework significantly improves the prediction accuracy by up to 3-11% compared to several alternative methods. The study also unveils several intriguing social phenomena from the data. For example, influence indeed exists among users for the paying behavior. The likelihood of a user becoming a new paying user is 5 times higher than chance when he has 5 paying neighbors of strong tie. We have deployed the proposed algorithm into the game, and the Lift_Ratio has been improved up to 196% compared to the prior strategy