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Practical decoy-state quantum secure direct communication

Science China(Physics,Mechanics & Astronomy) 2021年第12期64卷 68-75页

作者： Xin Liu Zijian Li Di Luo Chunfeng Huang Di Ma Minming Geng Junwei Wang Zhenrong Zhang Kejin Wei Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and TechnologyGuangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology School of ComputerElectronicsand InformationGuangxi UniversityNanning 530004China CAS Quantum Network Co. LtdShanghai 201315China

Quantum secure direct communication(QSDC) has been demonstrated in both fiber-based and free-space channels using attenuated lasers. Decoy-state QSDC by exploiting four decoy states has been proposed to address the problem of photon-numbersplitting attacks caused by the use of attenuated lasers. In this study, we present an analysis of the practical aspects of decoy-state QSDC. First, we design a two-decoy-state protocol that only requires two decoy states, thereby significantly reducing experimental complexity. Second, we successfully perform full parameter optimization for a real-life QSDC system by introducing a genetic algorithm. Our simulation results show that the two-decoy-state protocol could be the best choice for developing a practical QSDC system. Furthermore, full optimization is crucial for a high-performance QSDC system. Our work serves as a major step toward the further development of practical decoy-state QSDC systems.

关键词： quantum secure direct communication decoy-state method full parameter optimization

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Dual-Mode Resonant Sectorial Monopole Antenna with Stable Backfire Gain

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Chinese Journal of Electronics 2024年第6期33卷 1478-1486页

作者： Feiyan JI Heng ZHANG Xiuqiong XING Wenjun LU Lei ZHU Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau

A novel design approach to wideband, dual-mode resonant monopole antenna with stable, enhanced backfire gain is advanced. The sectorial monopole evolves from a linear, 0.75-wavelength electric prototype monopole under wideband dual-mode resonant operation. As theoretically predicted by the two resonant modes TE3/5,1and TE9/5,1within a 150° radiator, the operation principle is revealed at first. As have been numerically demonstrated and experimentally validated at 2.4-GHz band, the designed antenna exhibits a wide impedance bandwidth over 90.1%（i.e., 2.06–5.44 GHz）, in which the stable gain bandwidth in the backfire,-x-direction（θ = 90°, φ = 180°） with peak value of 3.2 dBi and fluctuation less than 3 dB is up to 45.3%（i.e., 3.74–5.44 GHz）. It is concluded that the stable wideband backfire gain frequency response should be owing to the high-order resonant mode in the unique sectorial monopole antennas.

关键词： Fluctuations Azimuth Prototypes Metals Resonance Frequency measurement Broadband antennas Impedance Gain Wideband

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Random mating mayfly algorithm for RFID network planning

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The Journal of China Universities of Posts and Telecommunications 2022年第5期29卷 40-50页

作者： Xie Xiaode Zheng Jiali Lin Zihan He Siyi Feng Minyu School of Computer Electrics and InformationGuangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi UniversityNanning 530004China

In order to improve robustness and efficiency of the radio frequency identification(RFID)network,a random mating mayfly algorithm(RMMA)was ***,RMMA introduced the mechanism of random mating into the mayfly algorithm(MA),which improved the population diversity and enhanced the exploration ability of the algorithm in the early stage,and find a better solution to the RFID nework planning(RNP)***,in RNP,tags are usually placed near the boundaries of the working space,so the minimum boundary mutation strategy was proposed to make sure the mayflies which beyond the boundary can keep the original search direction,as to enhance the ability of searching near the ***,in order to measure the performance of RMMA,the algorithm is then benchmarked on three well-known classic test functions,and the results are verified by a comparative study with particle swarm optimization(PSO),grey wolf optimization(GWO),and *** results show that the RMMA algorithm is able to provide very competitive results compared to these well-known meta-heuristics,RMMA is also applied to solve RNP *** performance evaluation shows that RMMA achieves higher coverage than the other three *** the number of readers is the same,RMMA can obtain lower interference and get a better load balance in each instance compared with other *** can also solve RNP problem stably and efficiently when the number and position of tags change over time.

关键词： radio frequency identification(RFID) RFID network planning(RNP) reader deployment mayfly algorithm(MA) random mating

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Detecting bot-infected machines based on analyzing the similar periodic DNS queries

Detecting bot-infected machines based on analyzing the simil...

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International Conference on Computing, Management and Telecommunications, ComManTel 2015

作者： Tu, Truong Dinh Guang, Cheng Xin, Liang Yi School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China Department of Information Technologies Tuyhoa Industrial College Phuyen Viet Nam

ISBN: (纸本)9781467365475

Modern botnets such as Zeus and Conficker com-monly utilize a technique called domain fluxing or a Domain Generation Algorithm (DGA) to generate a large number of pseudo-random domain names dynamically for botnet operators to control their bots. These botnets are becoming one of the most serious threats to the Internet security on a global scale. In this paper, we present a method based on analyzing the similar peri-odic time intervals series of DNS queries to identify DGA-bot infected machines. This method passively captures all DNS traffic from the gateway of monitor network. Firstly, we group queries of the same domain name that is requested by hosts, and then extracts time interval series between adjacent queries. Secondly, we measure the similar periodicity of DNS queries by calculating the squared Euclidean distance between each pair of their time interval series. Finally, we apply a hierarchical clustering algo-rithm to cluster high similar domain names. The experiment re-sults show that the domain names are generated by the same botnet or DGA would be grouped into the same cluster, thus all of the hosts that query to these clusters are marked as compro-mised hosts running a domain-flux botnet within monitor net-work. © 2015 IEEE.

关键词： Botnet

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RFID network planning based on improved brain storm optimization algorithm

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The Journal of China Universities of Posts and Telecommunications 2022年第5期29卷 30-39页

作者： Lin Zihan Zheng Jiali Xie Xiaode Feng Minyu He Siyi School of Computer Electrics and InformationGuangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi UniversityNanning 530004China

In order to improve the service quality of radio frequency identification(RFID) systems, multiple objectives should be comprehensively considered. An improved brain storm optimization algorithm GABSO, which incorporated adaptive learning operator and golden sine operator into the original brain storm optimization(BSO) algorithm, was proposed to solve the problem of RFID network planning(RNP). GABSO algorithm introduces learning operator and golden sine operator to achieve a balance between exploration and development. Based on GABSO algorithm, an optimization model is established to optimize the position of the reader. The GABSO algorithm was tested on the RFID model and dataset, and was compared with other methods. The GABSO algorithm’s tag coverage was increased by 9.62% over the Cuckoo search(CS) algorithm, and 7.70% over BSO. The results show that the GABSO algorithm could be successfully applied to solve the problem of RNP.

关键词： radio frequency identification(RFID) RFID network planning(RNP) brain storm optimization golden sine operator

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BALLKNN:An Efficient and Scalable KNN based on Euclidean Similarity

BALLKNN:An Efficient and Scalable KNN based on Euclidean Sim...

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International Joint Conference on Neural networks

作者： Peipei Lv Peng Yang Yongqiang Dong Liang Gu Key Laboratory of Computer Network and Information Integration (Southeast University)

ISBN: (纸本)9781509006212

The K Nearest Neighbor (KNN) technique is very simple, highly efficient and effective in the field of text cate-gorization, pattern recognition, object recognition etc. However, its efficiency is challenged by Big Data, which is characterized with volume, variety and velocity. All these characters require a new KNN algorithm with better time complexity, which is lower than O(n) at least. Additionally, the characters witness that centralized variants executed in a single thread are no longer reasonable. In this paper, we propose an efficient and scalable strategy, which we called as BALLKNN, for KNN classifier. In BALLKNN, since computation of some samples are reduced by replacing complicated quadratic sum with simple subduction and comparison, time efficiency improvement is obtained;the disadvantage of dependency on global data structure is overcome with the help of temporary variable(s), BALLKNN can be implemented with multiple threads easily, and it scales well with the size of the dataset. Meanwhile, as all samples are handled, the classification accuracy is guaranteed different from traditional sample reduction methods. Furthermore, detailed theoretical analysis on relationship between dimensionality of input and time complexity is provided. Finally, extensive experiments in large real datasets, with tens or hundreds of thousands of records and up to 618 dimensions, have demonstrated the efficiency and scalability of our methods.

关键词： complexity classes Scalability Data structures big data THREADS Theoretical analysis temporary variables Euclidean sample reduction Dataset

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Hybrid gray wolf optimization-cuckoo search algorithm for RFID network planning

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The Journal of China Universities of Posts and Telecommunications 2021年第6期28卷 91-102页

作者： Quan Yixuan Zheng Jiali Xie Xiaode Lin Zihan Luo Wencong School of Computer Electrics and InformationGuangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi UniversityNanning 530004China

In recent years,with the rapid development of Internet of things(IoT)technology,radio frequency identification(RFID)technology as the core of IoT technology has been paid more and more attention,and RFID network planning(RNP)has become the primary *** with the traditional methods,meta-heuristic method is widely used in *** at the target requirements of RFID,such as fewer readers,covering more tags,reducing the interference between readers and saving costs,this paper proposes a hybrid gray wolf optimization-cuckoo search(GWO-CS)*** method uses the input representation based on random gray wolf search and evaluates the tag density and location to determine the combination performance of the reader's propagation *** with particle swarm optimization(PSO)algorithm,cuckoo search(CS)algorithm and gray wolf optimization(GWO)algorithm under the same experimental conditions,the coverage of GWO-CS is 9.306%higher than that of PSO algorithm,6.963%higher than that of CS algorithm,and 3.488%higher than that of GWO *** results show that the GWO-CS algorithm cannot only improve the global search range,but also improve the local search depth.

关键词： radio frequency identification(RFID) gray wolf optimization(GWO)algorithm cuckoo search(CS)algorithm dynamic adjustment of discovery probability directional mutation

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NL2SQL Enhancement through Semantic Representation Aggregation 5

NL2SQL Enhancement through Semantic Representation Aggregati...

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5th International Conference on computer information Science and Application Technology, CISAT 2022

作者： Long, Lingli Zhu, Yongjin Shao, Jun Kong, Zheng Li, Jian Liu, Zejie Zhang, Xu Jiangsu Frontier Electric Technology Co. Ltd. China School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

ISBN: (纸本)9781510660076

Natural Language to SQL (NL2SQL) is a technology that converts natural language into executable SQL statements. For single table tasks, the dominant approach is a slot-filling-based model which actually performs multi-objective prediction. However, in existing multi-objective models, it is difficult to optimize multiple objectives simultaneously. Low correlation or conflict between various objectives can result in the model not being able to learn the parameters effectively. To effectively address this problem, this paper considers redesigning the sharing mechanism between multiple tasks and the specific network structure of individual tasks through a semantic aggregation approach in order to alleviate the above phenomenon. © 2022 SPIE.

关键词： Semantics

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NL2SQL Generation with Noise Labels based on Multi-task Learning 5

NL2SQL Generation with Noise Labels based on Multi-task Lear...

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2022 5th International Symposium on Big Data and Applied Statistics, ISBDAS 2022

作者： Long, Lingli Zhu, Yongjin Shao, Jun Kong, Zheng Li, Jian Xiang, Yanzheng Zhang, Xu Jiangsu Frontier Electric Technology Co. Ltd. China School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

With the rapid development of artificial intelligence technology, semantic recognition technology is becoming more and more mature, providing the preconditions for the development of natural language to SQL (NL2SQL) technology. In the latest research on NL2SQL, the use of pre-trained models as feature extractors for natural language and table schema has led to a very significant improvement in the effectiveness of the models. However, the current models do not take into account the degradation of the noisy labels on the overall SQL statement generation. It is crucial to reduce the impact of noisy labels on the overall SQL generation task and to maximize the return of accurate answers. To address this issue, we propose a restrictive constraint-based approach to mitigate the impact of noise-labeled labels on other tasks. In addition, parameter sharing approach is used in noiseless-labeled labels to capture each part's correlations and improve the robustness of the model. In addition, we propose to use Kullback-Leibler divergence to constrain the discrepancy between hard and soft constrained coding of noisy labels. Our model is compared with some recent state-of-the-art methods, and experimental results show a significant improvement over the approach in this paper. © Published under licence by IOP Publishing Ltd.

关键词： Semantics

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Calibration Bottleneck: Over-compressed Representations are Less Calibratable 41

Calibration Bottleneck: Over-compressed Representations are ...

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Deng-Bao Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing China Key Lab. of Computer Network and Information Integration Southeast University MOE China

Although deep neural networks have achieved remarkable success, they often exhibit a significant deficiency in reliable uncertainty calibration. This paper focus on model calibratability, which assesses how amenable a model is to be well recalibrated post-hoc. We find that the widely used weight decay regularizer detrimentally affects model calibratability, subsequently leading to a decline in final calibration performance after post-hoc calibration. To identify the underlying causes leading to poor calibratability, we delve into the calibratability of intermediate features across the hidden layers. We observe a U-shaped trend in the calibratability of intermediate features from the bottom to the top layers, which indicates that over-compression of the top representation layers significantly hinders model calibratability. Based on the observations, this paper introduces a weak classifier hypothesis, i.e., given a weak classification head that has not been over-trained, the representation module can be better learned to produce more calibratable features. Consequently, we propose a progressively layer-peeled training (PLP) method to exploit this hypothesis, thereby enhancing model calibratability. Our comparative experiments show the effectiveness of our method, which improves model calibration and also yields competitive predictive performance. Copyright 2024 by the author(s)

关键词： Deep neural networks

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