期刊：
Cryptography and Communications,2021年13(1):1-14 ISSN：1936-2447
通讯作者：
Luo, Jinquan
作者机构：
[Fang, Xiaolei; Luo, Jinquan; Liu, Meiqing] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China.;[Fang, Xiaolei; Luo, Jinquan; Liu, Meiqing] Cent China Normal Univ, Hubei Key Lab Math Sci, Wuhan 430079, Peoples R China.;[Fang, Xiaolei] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430048, Peoples R China.
通讯机构：
[Luo, Jinquan] C;Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Hubei Key Lab Math Sci, Wuhan 430079, Peoples R China.
摘要：
In this paper, we propose a mechanism for the construction of MDS codes with arbitrary dimensions of Euclidean hulls. Precisely, we construct (extended) generalized Reed-Solomon (GRS) codes with assigned dimensions of Euclidean hulls from self-orthogonal GRS codes. It turns out our constructions are more general than previous works on Euclidean hulls of (extended) GRS codes.
期刊：
Mathematical Problems in Engineering,2021年2021 ISSN：1024-123X
作者机构：
[Yang, M. J.; Sun, C. Q.; Liu, R. F.] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.;[Zeng, S.] Wuhan Polytech Univ, Sch Econ & Management, Wuhan 430023, Peoples R China.
摘要：
The research about online monitoring and leakage automatic location of water distribution networks (WDN) has a wide range of applications that include water resource protection, monitoring, and allocation. Variational mode decomposition (VMD) and cross-correlation (CC) based leakage location is a popular and effective method in WDN. However, the value of K intrinsic mode functions (IMFs) based on VMD decomposition needs to be determined artificially, which affects the separation effect of signal frequency band characteristics directly. Hence, this work proposes an adaptive method to determine the parameter K of leakage vibration signal's IMFs, which will be applied to automatic leakage location in WDN. Firstly, the number of saddle points in the frequency domain envelope of the sampled signal in different step sizes is calculated. The parameter K is determined according to the curvature change of the number of saddle points and the sampled signal. Finally, the selective IMFs are reconstituted into a new signal, which can determine a leak position using CC based time-delay estimation (TDE). To verify the effectiveness of the proposed algorithm, the different methods based on EMD and Fast ICA are compared. The experimental results demonstrate that the proposed parameter K value adaptive VMD (KVA-VMD) decomposition method is more suitable for leakage location in WDN.
摘要：
Recommender systems are widely used to provide users with items they may be interested in without explicitly searching. However, they suffer from low accuracy and scalability problems. Although existing clustering techniques have been incorporated to solve these inherent problems, most of them fail to achieve further improvement in recommendation accuracy because of ignoring the correlations between items and the different effects of item attributes on recommendation results. In this article, we propose a novel recommendation algorithm to alleviate these issues to a large extent. First of all, users and items are clustered into multiple cluster subsets based on user-item rating matrix and item attribute deriving from domain experts, respectively. Then we use a selection method relying on item attribute to mine candidate items and only their predictions will be calculated in the next step, which can save the computation time greatly. Furthermore, by weighting the predictions with TF-IDF (Term Frequency-Inverse Document Frequency) weights, the top-N recommendations are generated to the target user for return. Finally, comparative experiments on two real datasets demonstrate that this algorithm provides superior recommendation accuracy in terms of MAE (Mean Absolute Error) and RMSE (Root Mean Square Error).
作者机构：
[Xu, Xiangrui; Li, Yaqin; Yuan, Cao] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.
通讯机构：
[Yuan, Cao] W;Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.
关键词：
Deep neural network;ownership verification;security and privacy;serial number;watermarking
摘要：
The power of deep learning and the enormous effort and money required to build a deep learning model makes stealing them a hugely worthwhile and highly lucrative endeavor. Worse still, model theft requires little more than a high-school understanding of computer functions, which ensures a healthy and vibrant black market full of choice for any would-be pirate. As such, estimating how many neural network models are likely to be illegally reproduced and distributed in future is almost impossible. Therefore, we propose an embedded & x2018;identity bracelet & x2019; for deep neural networks that acts as proof of a model & x2019;s owner. Our solution is an extension to the existing trigger-set watermarking techniques that embeds a post-cryptographic-style serial number into the base deep neural network (DNN). Called a DNN-SN, this identifier works like an identity bracelet that proves a network & x2019;s rightful owner. Further, a novel training method based on non-related multitask learning ensures that embedding the DNN-SN does not compromise model performance. Experimental evaluations of the framework confirm that a DNN-SN can be embedded into a model when training from scratch or in the student network component of Net2Net.
摘要：
Traffic network transportation optimization problem (TNTOP) has important applications in logistics distribution fields. In various disciplines, methods about the solutions-termed TNTOP can have shown promising performance from different types of detection, at different conditions. Due to the limitatioins of the calculation speed of traditonal algorithms, it is rare that a simple unmodified method provides complete techniques of tackling large-scale TNTOP. We use the term P systems to solve the above limitatioins. Specifically, it is a tissue-like P system with four cells based on particle swarm algorithm, referred to as MPSO. In this system, the modified prim algorithm and the position-updated mechanism are adopted to generate and update all particle individuals, velocity-updated mechanism and an exchange-tree strategy are adopted to balance exploration and exploitation processes. Besides, some special strategies are also added to this systems. Numerous experiments are presented to verify the performance of the MPSO. The results show that it can generate the individuals of higher quality in shorter computation time when comparing to other benchmark algorithms. These empirical results validate the effectiveness and competitiveness of our proposed algorithm in solving TNTOP in terms of both quality and speed.
摘要：
Parameter estimation of variogram models is an important problem in geostatistics and environmental engineering. Most of existing works aim to estimate parameters of variogram single models, while neglecting the parameter estimation of variogram nested model. Most recently, the evolutionary algorithms(EA), including genetic algorithm(GA), are exploited to calculate the parameters of variogram model, which can obtain a more accurate solution. These methods have some hyper-parameters to set and suffer from the well-recognized premature convergence and slow global convergence problem of EA. In this paper, a double elite co-evolutionary genetic algorithm(DECGA) and deep reinforcement learning(dueling DQN) was introduced to estimate the parameters of variogram single or nested models so as to achieve better generalization performance. The DECGA can get the global optimal solution faster than GA with the help of dueling DQN, which can set the hyper-parameters according to the state of DECGA. To verify the effectiveness of the proposed method(DDQNGA), we conduct experiments on the agricultural heavy metal database. Experimental results demonstrate that our method can obtain parameter estimation more accurately. The method proposed in this paper have a certain practical value in the field of geostatistics and environmental engineering.
摘要：
Supporting the huge amount of network traffic from three-dimensional (3-D) streaming is an extremely important challenge, as 3-D streaming has the potential to cause network congestion and longer waiting times. These problems can also lead to 3-D video being blurred, and negatively affecting the user experience. This article uses the front camera of a mobile device to track a user's viewing angle, and then calculates the currently needed 3-D stream to find the most suitable peer for video source supply. This article also considers several impact factors to choose supply partners with different data flow via fuzzy theory. Consequently, the proposed method is able to determine the best way to download the desired 3-D stream without unnecessary congestion, thus maintaining the overall peer-to-peer network quality. Simulation results show that the proposed method can reduce the quantity of the video flow, while maintaining video quality, thus avoiding bottlenecks like bandwidth and network speed.
通讯机构：
[Tang, Xiaoyue] W;Wuhan Polytech Univ, Sch Math & Comp Sci, Jinyinhu Machi Rd, Wuhan 430024, Hubei, Peoples R China.
关键词：
BP neural network;cluster analysis;web user preference analysis;SOFM
摘要：
The paper uses BP neural network method to analyze the behavior preference of web users to achieve user behavior clustering, help advertisers to find online advertising design and optimize web design. At the same time, the paper analyzes the user preference cluster analysis, which helps users to find the desired webpage and content more conveniently, shorten the retrieval time and improve the retrieval efficiency. Firstly, the paper analyzes the log files of the web server, and then conducts session classification, finds the frequent data from the session vector, and normalizes the generated pattern vector. The BP SOFM model is used to cluster the user behavior preferences to generate users. Clustering. The experimental results show that BP neural network analysis method can effectively analyze user preferences and cluster user behavior.
作者机构：
[Zhang, Cong; Sun, Changqi] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.;[Xiong, Naixue] Northeastern State Univ, Dept Math & Comp Sci, Tahlequah, OK 74464 USA.
通讯机构：
[Zhang, Cong] W;[Xiong, Naixue] N;Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.;Northeastern State Univ, Dept Math & Comp Sci, Tahlequah, OK 74464 USA.
摘要：
Recently, the healthcare technologies continue to develop rapidly, especially various wearable Internet of Things (IoT) devices for body network have been invented one after another. The relevant products can already be easily purchased in the market such as the smart bracelet, smart blood pressure monitor and so on. These healthcare devices not only make users able to understand their own body information more in more detail but also provide a communication way to the hospital. It means that patients can obtain the professional medical prescription advice without going to the hospital in person because the health information can transmit to the medical cloud through any network interfaces. Additionally, both medical records of patients and prescription advice from doctors are stored in the cloud. In order to provide the better service quality, the use of fog in the network edge can quickly response the requests from the patients. The computing power of the fog node is less than the cloud. Therefore, balancing the trade-off between cloud and fog is very important. In this paper, we formulate an optimization problem about offloading then use the metaheuristic to find out the best policy. Moreover, we also design an emergency supporting measure. Simulation results show that the proposed methods can provide a more efficient healthcare service.
摘要：
This article is devoted to studying the initial-boundary value problem for an ideal polytropic model of non-viscous and compressible gas. We focus our attention on the outflow problem when the flow velocity on the boundary is negative and give a rigorous proof of the asymptotic stability of both the degenerate boundary layer and its superposition with the 3-rarefaction wave under some smallness conditions. New weighted energy estimates are introduced, and the trace of the density and velocity on the boundary are handled by some subtle analysis. The decay properties of the boundary layer and the smooth rarefaction wave also play an important role.
关键词：
Deep learning;Graph convolution network;Multi-scale graph;Precision farming;Site specific weed management
摘要：
Robotic weed control through weed detection has become increasingly important due to mounting pressure on herbicides from resistance and the large impact of weeds on agricultural productivity. One of the major challenges is accurate classification of weed species for selective targeting in crop situations, whilst the existing studies are often conducted in well-controlled settings with consistent lighting, species and backgrounds. Therefore, in this study, we propose a novel graph-based deep learning architecture, namely Graph Weeds Net (GWN), which aims to recognize multiple types of weeds from conventional RGB images collected from complex rangelands. GWN collects regional patterns in line with set image scopes and formulates multi-scale graph representations for weed classification. Additionally, GWN provides suggestions for key regions, creating opportunities for further within-image actions for robotic in-field systems. The architecture was evaluated on a recently published benchmark dataset, achieving the state-of-the-art performance with a top-1 accuracy 98.1%.
