The integration of sensing and communication can achieve ubiquitous sensing while enabling ubiquitous communication.Within the gradually improving global communication,the integrated sensing and communication system based on optical fibers can accomplish various functionalities,such as urban structure imaging,seismic wave detection,and pipeline safety monitoring.With the development of quantum communication,quantum networks based on optical fiber are gradually being established.In this paper,we propose an integrated sensing and quantum network(ISAQN)scheme,which can achieve secure key distribution among multiple nodes and distributed sensing under the standard quantum limit.The continuous variables quantum key distribution protocol and the round-trip multiband structure are adopted to achieve the multinode secure key distribution.Meanwhile,the spectrum phase monitoring protocol is proposed to realize distributed sensing.It determines which node is vibrating by monitoring the frequency spectrum and restores the vibration waveform by monitoring the phase change.The scheme is experimentally demonstrated by simulating the vibration in a star structure network.Experimental results indicate that this multiuser quantum network can achieve a secret key rate of approximately 0.7 Mbits/s for each user under 10-km standard fiber transmission,and its network capacity is 8.In terms of distributed sensing,it can achieve a vibration response bandwidth ranging from 1 Hz to 2 kHz,a strain resolution of 0.50 nε/Hz,and a spatial resolution of 0.20 m under shot-noise-limited detection.The proposed ISAQN scheme enables simultaneous quantum communication and distributed sensing in a multipoint network,laying a foundation for future large-scale quantum networks and high-precision sensing networks.
With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.
Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.
Information networks store rich information in the nodes and edges,which benefit many downstream tasks,such as recommender systems and knowledge graph completion.Information networks contain homogeneous information,heterogeneous information and knowledge graphs.A significant number of surveys focus on one of the three parts and summarize the research works,but few surveys conclude and compare the three kinds of information networks.In addition,in real scenarios,lots of information networks lack sufficient labeled data,so the combination of meta-learning and information networks can bring in extended applications.This paper concentrates on few-shot information networks and systematically presents recent works to help analyze and follow related works.
Friendship paradox states that individuals are likely to have fewer friends than their friends do,on average.Despite of its wide existence and appealing applications in real social networks,the mathematical understanding of friendship paradox is very limited.Only few works provide theoretical evidence of single-step and multi-step friendship paradoxes,given that the neighbors of interest are onehop and multi-hop away from the target node.However,they consider non-evolving networks,as opposed to the topology of real social networks that are constantly growing over time.We are thus motivated to present a first look into friendship paradox in evolving networks,where newly added nodes preferentially attach themselves to those with higher degrees.Our analytical verification of both single-step and multistep friendship paradoxes in evolving networks,along with comparison to the non-evolving counterparts,discloses that“friendship paradox is even more paradoxical in evolving networks”,primarily from three aspects:1)we demonstrate a strengthened effect of single-step friendship paradox in evolving networks,with a larger probability(more than 0.8)of a random node’s neighbors having higher average degree than the random node itself;2)we unravel higher effectiveness of multi-step friendship paradox in seeking for influential nodes in evolving networks,as the rate of reaching the max degree node can be improved by a factor of at least Θ(t^(2/3))with t being the network size;3)we empirically verify our findings through both synthetic and real datasets,which suggest high agreements of results and consolidate the reasonability of evolving model for real social networks.
In Power Line Communications(PLC),there are regulatory masks that restrict the transmit power spectral density for electromagnetic compatibility reasons,which creates coverage issues despite the not too long distances.Hence,PLC networks often employ repeaters/relays,especially in smart grid neighborhood area networks.Even in broadband indoor PLC systems that offer a notable data rate,relaying may pave the way to new applications like being the backbone for wireless technologies in a cost-effective manner to support the Internet-of-things paradigm.In this paper,we study Multiple-Input Multiple-Output(MIMO)PLC systems that incorporate inband full-duplex functionality in relaying networks.We present several MIMO configurations that allow end-to-end half-duplex or full-duplex operations and analyze the achievable performance with state-of-the-art PLC systems.To reach this analysis,we get channel realizations from random network layouts for indoor and outdoor scenarios.We adopt realistic MIMO channel and noise models and consider transmission techniques according to PLC standards.The concepts discussed in this work can be useful in the design of future PLC relay-aided networks for different applications that look for a coverage extension and/or throughput:smart grids with enhanced communications in outdoor scenarios,and“last meter”systems for high-speed connections everywhere in indoor ones.
