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：
When the model begins a new task, the challenge of naming the "catastrophic forgetting" limits the scalability of the deep learning network, which quickly forgets the learning capabilities it has. The fine-tuning method recommends that the original feature extraction be retained to extract the features of the new task and to achieve the purpose of learning the new class. However, this method degrades performance on previously learned tasks because the shared parameters change without new guidance for the original task-specific prediction parameters. This paper proposes general fine-tune method to reduce catastrophic forgetting in sequential task learning scenarios. The critical idea of the method is fine-tuning the parameters in each layer, unlike the traditional fine tuning only for the last layer. The experimental results show that the new method is superior to fine-tune, in the accuracy of the old task and the performance of the new task is better than that of the EWC. A distinct advantage is that old tasks do not limit the performance of new tasks but provide some support for new tasks.
通讯机构：
[Xu, Xiangrui] W;Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Hubei, Peoples R China.
会议名称：
MIPPR 2019: Automatic Target Recognition and Navigation
会议时间：
Wuhan, China
会议地点：
Wuhan, PEOPLES R CHINA
会议主办单位：
[Xu, Xiangrui;Li, Yaqin;Gao, Yunlong;Yuan, Cao] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Hubei, Peoples R China.
会议论文集名称：
MIPPR 2019: Automatic Target Recognition and Navigation
关键词：
Deep neural network;identity number (ID);Ownership verification
摘要：
Amid the maturity of machine learning, deep neural networks are gradually applied in the business sector rather than be restricted in the laboratory. However, its intellectual property protection encounters a significant challenge. In this paper, we aim at embedding a unique identity number (ID) to the deep neural network for model ownership verification. To this end, a scheme of generating DNN ID is proposed, which is the criterion for model ownership verification. After embedding, the model can complete the original performance and own a unique ID of this model as well. DNN ID can only be generated by the owner to check the model authorship. We evaluate this method on MNIST. Experiment results demonstrate that the DNN ID can accurately verify the ownership of our trained model.
期刊：
Advances in Computer Science Research,2017年75:467-470 ISSN：2352-538X
通讯作者：
Yuan, Cao
作者机构：
[Sun, Jianchi; Yuan, Cao] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Hubei, Peoples R China.
通讯机构：
[Yuan, Cao] W;Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Hubei, Peoples R China.
会议名称：
7th International Conference on Mechatronics, Computer and Education Informationization (MCEI)
会议时间：
NOV 03-05, 2017
会议地点：
Shenyang, PEOPLES R CHINA
会议主办单位：
[Sun, Jianchi;Yuan, Cao] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Hubei, Peoples R China.
会议论文集名称：
ACSR-Advances in Comptuer Science Research
关键词：
Feature extraction;Cotton leaf;Analyze and compare;Operator
摘要：
Cotton is the main plant of braided fabric in China, the production of cotton planting is important, but cotton are caused by diseases and insect pests easily in planting, so in the process of farming, some useful information of cotton diseases should be achieved easily and can be analyzed effectively, so some accurate and effective actions can be done. In the paper, cotton leaf as the research object, the main diseases are introduced, how to detect the main diseases is the important step in modern agricultural digital technology. In the paper, cotton leaf as the object of research, the main features of apple leafs should be reserved, and the special features of the disease region in apple leaf should be emphasized effective. The novel algorithm based on color components and edge extraction algorithms. More than 500 results of main cotton image with the main diseases can be compared by main operator algorithms and color components, analyze the results of different cotton diseases and operators, the new algorithm based on color difference and morphological features be precedence than other algorithms in the main cotton diseases.
期刊：
PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON ELECTRONIC, MECHANICAL, INFORMATION AND MANAGEMENT SOCIETY (EMIM),2016年40:1035-1040 ISSN：2352-538X
通讯作者：
Li, Yaqin
作者机构：
[Li, Yaqin; Gong, Cheng; Yuan, Cao] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Peoples R China.
通讯机构：
[Li, Yaqin] W;Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Peoples R China.
会议名称：
6th International Conference on Electronic, Mechanical, Information and Management Society (EMIM)
会议时间：
APR 01-03, 2016
会议地点：
Shenyang, PEOPLES R CHINA
会议主办单位：
[Li, Yaqin;Yuan, Cao;Gong, Cheng] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan, Peoples R China.
会议论文集名称：
ACSR-Advances in Comptuer Science Research
关键词：
Online-teaching;PCI;FPGA;DSP
摘要：
These online-teaching database systems are generated from online transactions, emails, videos, audios, images. They are stored in databases grow massively and become difficult to capture, form, store, manage, share, analyze and visualize via typical database software tools. The adventure of large amount of data and real-time requirements presents great challenges using traditional desktop computers. In this paper, to tackle the specific problem with a critical requirement of 10ms for whole data processing pipeline, we proposed a high performance embedded system as opposed to personal computer. Peripheral Component Interconnect (PCI) for data transferring from computer to Field Programmable Gate Array (FPGA) is proposed to solve the problem, and a custom-designed dual-port dual-channel RAM to realize simultaneous data exchange and data processing which was implemented using a 6-core Digital Signal Processor (DSP). The hardware system was designed and tested the performance of individual modules as well as the integration of them as a whole. We reported a total time using such pipeline of 7.5ms, meeting the critical requirement and demonstrating its feasibility in practical application.
摘要：
With the development of information and the integration of media, it has great practical significance and research value to build a digital learning environment based on the complicated electronic circuit. However, the complicated electronic circuit in real-time need a complex and expensive technology. In order to overcome the high cost and technology, an approach was proposed for simplifying generation by approximating the excitations with rectangular pulses, triangular pulses and cosine waves which can be implemented with a moderate cost in analogical electronics. In this work, we improved a novel approach based on genetic programming, The differences between theoretical excitation signals and the approximation driving pulses, related to their excitation effects, were minimized by genetic programming. From these results, the accuracy of simulation can be improved by the new approach, the difference between theoretical complicated digital signals and the new approach is reduced. A trade off is obtained between the costs of implementation of digital processing in digital learning environments.
会议论文集名称：
2015 International Conference of Educational Innovation through Technology (EITT)
关键词：
teaching evaluation;method of weighted mean;classification
摘要：
The paper selects certain features then classifies students and calculates the average score of each type student, ultimately by means of the method of weighted mean, gives each type of student a certain weight, to process the data of teaching evaluation. Compared with traditional method, the results of above method are closer to teacher's true level, and the data processing is also more scientific and reasonable. This paper also points out the disadvantages of current assessment system and recommends universities can adopt the method of weighted mean to improve the accuracy and effectiveness in current system.
摘要：
For a graph G, the Hosoya index and the Merrifield Simmons index are defined as the total number of its matchings and the total number of its independent sets, respectively. In this paper, we characterize the structure of those graphs that minimize the Merrifield-Simmons index and those that maximize the Hosoya index in two classes of simple connected graphs with n vertices: graphs with fixed matching number and graphs with fixed connectivity. (C) 2014 Elsevier B.V. All rights reserved.
作者：
Zhu, Tianqing;Li, Gang;Zhou, Wanlei;Xiong, Ping;Yuan, Cao
期刊：
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),2014年8443 LNAI(PART 1):557-568 ISSN：0302-9743
通讯作者：
Li, G.(gang.li@deakin.edu.au)
作者机构：
[Zhou, Wanlei; Li, Gang; Yuan, Cao; Zhu, Tianqing; Xiong, Ping] School of Information Technology, Deakin University, Australia;[Zhou, Wanlei; Li, Gang; Yuan, Cao; Zhu, Tianqing; Xiong, Ping] School of Mathematics and Computer, Wuhan Polytechnic University, China;[Zhou, Wanlei; Li, Gang; Yuan, Cao; Zhu, Tianqing; Xiong, Ping] School of Information, Zhongnan University of Economics and Law, China
会议名称：
18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014
会议时间：
13 May 2014 through 16 May 2014
会议地点：
Tainan
关键词：
Data mining;Background information;Differential privacies;Privacy concerns;Privacy preserving;Real-world datasets;Recommendation;Tagging;Weight perturbation;User interfaces
期刊：
Applied Mechanics and Materials,2013年329:382-386 ISSN：1660-9336
作者机构：
[Li Y.Q.; Chen D.] School of Mathematic and Computer Science, Wuhan Polytechnic University, China;[Yuan C.] School of Computer and Software, Wuhan Vocational College of Software and Engineering, China
会议名称：
3rd International Conference on Intelligent Structure and Vibration Control, ISVC 2013
会议时间：
22 March 2013 through 24 March 2013
关键词：
Arbitrary waveform generator;Direct digital synthesizer;Hard system;Limited diffracting wave
摘要：
Let Diag(G) and D(G) be the degree-diagonal matrix and distance matrix of G, respectively. Define the multiplier Diag(G)D(G) as degree distance matrix of G. The degree distance of G is defined as D'(G) = Σ x∈v(G)dG(x)Dg(x), where dg(x) is the degree of vertex x, DG(x) = Σ∈V(G)d G(u,x) and dg(u,x) the distance between u and x. Obviously, D'(G) is also the sum of elements of degree distance matrix Diag(G)D(G) of G. A connected graph G is a cactus if any two of its cycles have at most one common vertex. Let G (n, r) be the set of cacti of order n and with r cycles. In this paper, we give the sharp lower bound of the degree distance of cacti among G (n,r), and characterize the corresponding extremal cactus.
