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The energy thresholding characteristic of carbon dioxide (CO2) Cherenkov gas is significant for the detection of fusion $\gamma $ -rays. In this article, the energy thresholding characteristic of CO2 (at 300 K and 4.02 atm.) gas is experimentally calibrated by a pulse electron beam with picosecond duration time and adjustable energies. Three mirrors with the ultraviolet reflectivity are used to make an off-axis light collection system. For electron energy of 10 MeV (above the Cherenkov threshold energy), the collected pulse photon spectrum has good agreement with the theoretical spectrum of Cherenkov radiation, confirming that Cherenkov radiation dominates collected photons than non-Cherenkov sources. The threshold energy resolution (12/7.5 MeV) is improved to ~104, an order higher than the reported value of ~103. The improvement on the threshold energy resolution of CO2 gas is mainly attributed to the ultraviolet mirrors, suppressing lights with a wavelength higher than 280 nm. This work can provide significant support for the further application of the Cherenkov detector in the fusion $\gamma $ -ray diagnosis. [ABSTRACT FROM AUTHOR]

Adaptive bitrate streaming (ABR) has become the de facto technique for video streaming over the Internet. Despite a flurry of techniques, achieving high quality ABR streaming over cellular networks remains a tremendous challenge. ABR streaming can be naturally modeled as a control problem. There has been some initial work on using PID, a widely used feedback control technique, for ABR streaming. Existing studies, however, either use PID control directly without fully considering the special requirements of ABR streaming, leading to suboptimal results, or conclude that PID is not a suitable approach. In this paper, we take a fresh look at PID-based control for ABR streaming. We design a framework called PIA (PID-control based ABR streaming) that strategically leverages PID control concepts and incorporates several novel strategies to account for the various requirements of ABR streaming. We evaluate PIA using simulation based on real LTE network traces, as well as using real DASH implementation. The results demonstrate that PIA outperforms state-of-the-art schemes in providing high average bitrate with significantly lower bitrate changes (reduction up to 40 percent) and stalls (reduction up to 85 percent), while incurring very small runtime overhead. We further design PIA-E (PIA Enhanced), which improves the performance of PIA in the important initial playback phase. [ABSTRACT FROM AUTHOR]

Recommender systems have been increasingly used in a variety of web services, providing a list of recommended items in which a user may have an interest. While important, recommender systems are vulnerable to various malicious attacks. In this paper, we study a new security vulnerability in recommender systems caused by web injection, through which malicious actors stealthily tamper any unprotected in-transit HTTP webpage content and force victims to visit specific items in some web services (even running HTTPS), e.g., YouTube. By doing so, malicious actors can promote their targeted items in those web services. To obtain a deeper understanding on the recommender systems of our interest (including YouTube, Yelp, Taobao, and 360 App market), we first conduct a measurement-based analysis on several real-world recommender systems by leveraging machine learning algorithms. Then, web injection is implemented in three different types of devices (i.e., computer, router, and proxy server) to investigate the scenarios where web injection could occur. Based on the implementation of web injection, we demonstrate that it is feasible and sometimes effective to manipulate the real-world recommender systems through web injection. We also present several countermeasures against such manipulations. [ABSTRACT FROM AUTHOR]

Video streaming today accounts for up to 55% of mobile traffic. In this paper, we explore streaming videos encoded using scalable video coding (SVC) scheme over highly variable bandwidth conditions, such as cellular networks. SVC’s unique encoding scheme allows the quality of a video chunk to change incrementally, making it more flexible and adaptive to challenging network conditions compared to other encoding schemes. Our contribution is threefold. First, we formulate the quality decisions of video chunks constrained by the available bandwidth, the playback buffer, and the chunk deadlines as an optimization problem. The objective is to optimize a novel quality-of-experience metric that models a combination of the three objectives of minimizing the stall/skip duration of the video, maximizing the playback quality of every chunk, and minimizing the number of quality switches. Second, we develop layered bin packing (LBP) adaptation algorithm, a novel algorithm that solves the proposed optimization problem. Moreover, we show that LBP achieves the optimal solution of the proposed optimization problem with linear complexity in the number of video chunks. Third, we propose an online algorithm (online LBP) where several challenges are addressed, including handling bandwidth prediction errors and short prediction duration. Extensive simulations with real bandwidth traces of public datasets reveal the robustness of our scheme and demonstrate its significant performance improvement as compared with the state-of-the-art SVC streaming algorithms. The proposed algorithm is also implemented on a TCP/IP emulation test bed with real LTE bandwidth traces, and the emulation confirms the simulation results and validates that the algorithm can be implemented and deployed on today’s mobile devices. [ABSTRACT FROM AUTHOR]