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Xinxin Zhang,Peifeng Niu,Nan Liu,Guoqiang Li 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.2
This paper deals with the synchronization issue of fractional-order complex-valued Hopfield neural networkswith time delay. In this paper, by means of properties of the fractional-order inequality, such as H¨ olderinequality and Gronwall inequality, sufficient conditions are presented to guarantee the finite-time synchronizationof the fractional-order complex-valued delayed neural networks when 1=2 g < 1 and 0 <g < 1=2. Finally, twonumerical simulations are provided to show the effectiveness of the obtained results.
Current compensation for material consumption of cobalt self-powered neutron detector
Liu, Xinxin,Wang, Zhongwei,Zhang, Qingmin,Deng, Bangjie,Niu, Yaobin Korean Nuclear Society 2020 Nuclear Engineering and Technology Vol.52 No.4
Co Self-Powered Neutron Detector (SPND) is confronted with the problem of material consumption, which causes the response current can neither reflect the change of neutron flux in time nor be proportional to the neutron flux. In this paper, a deconvolution-based method is established to solve this problem. First of all, a step signal of neutron flux is taken as an example to analyze its performance. When the material consumption of Co SPND is 10%, after compensation, the response current can be in correspondence of neutron flux. Finally, the effects of this model in different Signal-to-Noise Ratio are analyzed, which fully confirms the truth of its excellent performance for compensating Co SPND's signal.
Geographic atrophy segmentation in SD-OCT images using synthesized fundus autofluorescence imaging
Wu, Menglin,Cai, Xinxin,Chen, Qiang,Ji, Zexuan,Niu, Sijie,Leng, Theodore,Rubin, Daniel L.,Park, Hyunjin ELSEVIER SCIENTIFIC PUBLISHERS IRELAND LTD 2019 COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE Vol.182 No.-
<P><B>Abstract</B></P> <P><B>Background and objective</B></P> <P>Accurate assessment of geographic atrophy (GA) is critical for diagnosis and therapy of non-exudative age-related macular degeneration (AMD). Herein, we propose a novel GA segmentation framework for spectral-domain optical coherence tomography (SD-OCT) images that employs synthesized fundus autofluorescence (FAF) images.</P> <P><B>Methods</B></P> <P>An en-face OCT image is created via the restricted sub-volume projection of three-dimensional OCT data. A GA region-aware conditional generative adversarial network is employed to generate a plausible FAF image from the en-face OCT image. The network balances the consistency between the entire synthesize FAF image and the lesion. We use a fully convolutional deep network architecture to segment the GA region using the multimodal images, where the features of the en-face OCT and synthesized FAF images are fused on the front-end of the network.</P> <P><B>Results</B></P> <P>Experimental results for 56 SD-OCT scans with GA indicate that our synthesis algorithm can generate high-quality synthesized FAF images and that the proposed segmentation network achieves a dice similarity coefficient, an overlap ratio, and an absolute area difference of 87.2%, 77.9%, and 11.0%, respectively.</P> <P><B>Conclusion</B></P> <P>We report an automatic GA segmentation method utilizing synthesized FAF images.</P> <P><B>Significance</B></P> <P>Our method is effective for multimodal segmentation of the GA region and can improve AMD treatment.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Novel geographic atrophy (GA) segmentation for spectral-domain optical coherence tomography. </LI> <LI> Our approach uses synthesized fundus autofluorescence images to aid the segmentation. </LI> <LI> Our method can improve the segmentation performance of the GA. </LI> </UL> </P>
Multiparty Access Control of Ciphertext Sharing in Cloud-Based Online Social Networks
Huang Qinlong,Ma Zhaofeng,Yang Yixian,Niu Xinxin 보안공학연구지원센터 2015 International Journal of Security and Its Applicat Vol.9 No.12
Although current online social networks (OSNs) schemes propose to encrypt data before sharing, the enforcement of access policies over encrypted data has become a challenging task, and the OSNs currently do not provide any mechanism to allow users to update access policies. In this paper, we propose a ciphertext sharing scheme in cloud-based OSNs, which allows the users to outsource encrypted data to the OSNs service provider for sharing. In order to meet the authorization requirement, we present a multiparty access control model based on ciphertext-policy attribute-based proxy re-encryption, which enables the access control of encrypted data associated with multiple users. On the basis of ciphertext-policy attribute-based encryption, the owners can customize the access policy of their own data. Based on proxy re-encryption, the disseminators such as friends and group members can further customize the access policy of the owners’ data upon existing access policy. Besides, we achieve immediate user revocation based on secret sharing without issuing new attribute secret keys to unrevoked users. The security and performance analysis show that our proposed scheme is secure, efficient and practical.
Defense Strategy of Network Security based on Dynamic Classification
( Jinxia Wei ),( Ru Zhang ),( Jianyi Liu ),( Xinxin Niu ),( Yixian Yang ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.12
In this paper, due to the network security defense is mainly static defense, a dynamic classification network security defense strategy model is proposed by analyzing the security situation of complex computer network. According to the network security impact parameters, eight security elements and classification standard are obtained. At the same time, the dynamic classification algorithm based on fuzzy theory is also presented. The experimental analysis results show that the proposed model and algorithm are feasible and effective. The model is a good way to solve a safety problem that the static defense cannot cope with tactics and lack of dynamic change.
Feature Selection to Mine Joint Features from High-dimension Space for Android Malware Detection
( Yanping Xu ),( Chunhua Wu ),( Kangfeng Zheng ),( Xinxin Niu ),( Tianling Lu ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.9
Android is now the most popular smartphone platform and remains rapid growth. There are huge number of sensitive privacy information stored in Android devices. Kinds of methods have been proposed to detect Android malicious applications and protect the privacy information. In this work, we focus on extracting the fine-grained features to maximize the information of Android malware detection, and selecting the least joint features to minimize the number of features. Firstly, permissions and APIs, not only from Android permissions and SDK APIs but also from the developer-defined permissions and third-party library APIs, are extracted as features from the decompiled source codes. Secondly, feature selection methods, including information gain (IG), regularization and particle swarm optimization (PSO) algorithms, are used to analyze and utilize the correlation between the features to eliminate the redundant data, reduce the feature dimension and mine the useful joint features. Furthermore, regularization and PSO are integrated to create a new joint feature mining method. Experiment results show that the joint feature mining method can utilize the advantages of regularization and PSO, and ensure good performance and efficiency for Android malware detection.
New Public Key Encryption with Equality Test Based on non-Abelian Factorization Problems
( Huijun Zhu ),( Licheng Wang ),( Shuming Qiu ),( Xinxin Niu ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.2
In this paper, we present a new public key encryption scheme with equality test (PKEwET). Compared to other PKEwET schemes, we find that its security can be improved since the proposed scheme is based on non-Abelian factorization problems. To our knowledge, it is the first scheme regarding equality test that can resist quantum algorithm attacks. We show that our scheme is one-way against chosen-ciphertext attacks in the case that the computational Diffie-Hellman problem is hard for a Type-I adversary. It is indistinguishable against chosen-ciphertext attacks in the case that the Decisional Diffie-Hellman problem is hard in the random oracle model for a Type-II adversary. To conclude the paper, we demonstrate that our scheme is more efficient.
A (k,t,n) verifiable multi-secret sharing scheme based on adversary structure
( Jing Li ),( Licheng Wang ),( Jianhua Yan ),( Xinxin Niu ),( Yixian Yang ) 한국인터넷정보학회 2014 KSII Transactions on Internet and Information Syst Vol.8 No.12
A (n,t,n) secret sharing scheme is to share a secret among n group members, where each member also plays a role of a dealer,and any t shares can be used to recover the secret. In this paper, we propose a strong (k,t,n) verifiable multi-secret sharing scheme, where any k out of n participants operate as dealers. The scheme realizes both threshold structure and adversary structure simultaneously, and removes a trusted third party. The secret reconstruction phase is performed using an additive homomorphism for decreasing the storage cost. Meanwhile, the scheme achieves the pre-verification property in the sense that any participant doesn`t need to reveal any information about real master shares in the verification phase. We compare our proposal with the previous (n,t,n) secret sharing schemes from the perspectives of what kinds of access structures they achieve, what kinds of functionalities they support and whether heavy storage cost for secret share is required. Then it shows that our scheme takes the following advantages: (a) realizing the adversary structure, (b) allowing any k out of n participants to operate as dealers, (c) small sized secret share. Moreover, our proposed scheme is a favorable candidate to be used in many applications, such as secure multi-party computation and privacy preserving data mining, etc.
Flow-based Anomaly Detection Using Access Behavior Profiling and Time-sequenced Relation Mining
( Weixin Liu ),( Kangfeng Zheng ),( Bin Wu ),( Chunhua Wu ),( Xinxin Niu ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.6
Emerging attacks aim to access proprietary assets and steal data for business or political motives, such as Operation Aurora and Operation Shady RAT. Skilled Intruders would likely remove their traces on targeted hosts, but their network movements, which are continuously recorded by network devices, cannot be easily eliminated by themselves. However, without complete knowledge about both inbound/outbound and internal traffic, it is difficult for security team to unveil hidden traces of intruders. In this paper, we propose an autonomous anomaly detection system based on behavior profiling and relation mining. The single-hop access profiling model employ a novel linear grouping algorithm PSOLGA to create behavior profiles for each individual server application discovered automatically in historical flow analysis. Besides that, the double-hop access relation model utilizes in-memory graph to mine time-sequenced access relations between different server applications. Using the behavior profiles and relation rules, this approach is able to detect possible anomalies and violations in real-time detection. Finally, the experimental results demonstrate that the designed models are promising in terms of accuracy and computational efficiency.