Increased demand for DNS privacy has driven the creation of several encrypted DNS protocols, such as DNS over HTTPS (DoH), DNS over TLS (DoT), and DNS over QUIC (DoQ). Recently, DoT and DoH have been deployed by some vendors like Google and Cloudflare. This paper addresses privacy leakage in these three encrypted DNS protocols (especially DoQ) with different DNS recursive resolvers (Google, NextDNS, and Bind) and DNS proxy (AdGuard). More particularly, we investigate encrypted DNS traffic to determine whether the adversary can infer the category of websites users visit for this purpose. Through analyzing packet traces of three encrypted DNS protocols, we show that the classification performance of the websites (i.e., user's privacy leakage) is very high in terms of identifying 42 categories of the websites both in public (Google and NextDNS) and local (Bind) resolvers. By comparing the case with cache and without cache at the local resolver, we confirm that the caching effect is negligible as regards identification. We also show that discriminative features are mainly related to the inter-arrival time of packets for DNS resolving. Indeed, we confirm that the F1 score decreases largely by removing these features. We further investigate two possible countermeasures that could affect the inter-arrival time analysis in the local resolver: AdBlocker and DNS prefetch. However, there is no significant improvement in results with these countermeasures. These findings highlight that information leakage is still possible even in encrypted DNS traffic regardless of underlying protocols (i.e., HTTPS, TLS, QUIC).

Chinese Named Entity Recognition is the fundamental technology in the field of the Chinese Natural Language Process. It is extensively adopted into information extraction, intelligent question answering, and knowledge graph. Nevertheless, due to the diversity and complexity of Chinese, most Chinese NER methods fail to sufficiently capture the character granularity semantics, which affects the performance of the Chinese NER. In this work, we propose DSKE-Chinese NER: Chinese Named Entity Recognition based on Dictionary Semantic Knowledge Enhancement. We novelly integrate the semantic information of character granularity into the vector space of characters and acquire the vector representation containing semantic information by the attention mechanism. In addition, we verify the appropriate number of semantic layers through the comparative experiment. Experiments on public Chinese datasets such as Weibo, Resume and MSRA show that the model outperforms character-based LSTM baselines.

A highly integrated monolithic Multi-Service Transport Platform (MSTP) ASIC MSEOSX8-6 incorporating more than 26M transistors has been fabricated with 0.18 µm CMOS technology. The chip is a powerful monolithic MSTP ASIC that supports RPR applications and serves as a generic building block for MSTP network. To accelerate the chip design, we devise a novel methodology called Embedded Reduced Self-Tester (ERST), which integrates the reduced self-tester structure into the chip to shorten the duration of dynamic simulation. Moreover, we divide the design into 12 smaller Hierarchical Layout Blocks (HLB) to enable parallel layout. Resultantly, the whole design has been completed in 5 months, which saves at least 80% of the design cycle in all.

Being a new feature of next generation of wireless networks, Mobile Multi-hop Relay (MMR) is proposed for the purpose of coverage extension and throughput enhancement in LTE-Advanced, IEEE 802.16 j/m. Besides, with the help of relay, the system energy consumption could be well saved. In this paper, an energy saving scheduling scheme is proposed for OFDMA based two-hop relay systems. The novel scheme adjusts the modulation and coding (MC) mode and allocates the transmit power dynamically according to the resource intensity. It can also guarantee the Quality of Service (QoS) of different services by setting the scheduling priority. The simulation results show that the novel scheduling scheme can reduce energy consumption up to 76.27% compared to the conventional scheduling scheme, and achieve higher throughput while guaranteeing QoS.