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DCEL:classifier fusion model for Android malware detection

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Journal of Systems Engineering and Electronics 2024年第1期35卷 163-177页

作者： XU Xiaolong JIANG Shuai ZHAO Jinbo wang xinheng Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Computing and Engineering University of West LondonLondon W55RFUK

The rapid growth of mobile applications,the popularity of the Android system and its openness have attracted many hackers and even criminals,who are creating lots of Android malware.However,the current methods of Android malware detection need a lot of time in the feature engineering phase.Furthermore,these models have the defects of low detection rate,high complexity,and poor practicability,etc.We analyze the Android malware samples,and the distribution of malware and benign software in application programming interface(API)calls,permissions,and other attributes.We classify the software’s threat levels based on the correlation of features.Then,we propose deep neural networks and convolutional neural networks with ensemble learning(DCEL),a new classifier fusion model for Android malware detection.First,DCEL preprocesses the malware data to remove redundant data,and converts the one-dimensional data into a two-dimensional gray image.Then,the ensemble learning approach is used to combine the deep neural network with the convolutional neural network,and the final classification results are obtained by voting on the prediction of each single classifier.Experiments based on the Drebin and Malgenome datasets show that compared with current state-of-art models,the proposed DCEL has a higher detection rate,higher recall rate,and lower computational cost.

关键词： Android malware detection deep learning ensemble learning model fusion

Uncovering the action mechanism of Shenqi Tiaoshen formula(参芪调肾方) in the treatment of chronic obstructive pulmonary disease through network pharmacology,molecular docking,and experimental verification

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Journal of Traditional Chinese Medicine 2024年第4期 770-783页

作者： YANG Qinjun YIN Dandan wang Hui GAO Yating wang xinheng WU Di TONG Jiabing wang Chuanbo LI Zegeng School of Chinese Medicine Anhui University of Chinese Medicine Anhui Province Key Laboratory of the Application and Transformation of Traditional Chinese Medicine in the Prevention and Treatment of Major Pulmonary Diseases Department of Pharmacy the First Affiliated Hospital of Anhui Medical University Institute of Traditional Chinese Medicine Respiratory Disease Prevention and Control Anhui Academy of Traditional Chinese Medicine Department of Respiratory the First Affiliated Hospital of Anhui University of Chinese Medicine Department of Chinese Medicine the Second Affiliated Hospital of Anhui Medical University

OBJECTIVE: To reveal the potential underlying mechanism of the Shenqi Tiaoshen formula(参芪调肾方, SQTS) in the treatment of chronic obstructive pulmonary disease(COPD) by utilizing network pharmacology, molecular docking, and experimental verification. METHODS: Multiple open-source databases and research related to Traditional Chinese Medicine or compounds were employed to screen active ingredients and corresponding potential targets of the SQTS. The protein-protein interaction network screened hub genes, the relevant molecular mechanism and gene regulation were initially identified through the Kyoto Encyclopedia of Genes and Genomes(KEGG) and Gene Ontology(GO) analysis, and molecular docking was used to confirm further the interaction of the main components bound to the core targets. In vivo experiments on the COPD combined Qi-deficiency syndrome rat model were performed to verify the intervention effects and predicted potential molecular mechanisms of the SQTS. RESULTS: This study selected 156 active compounds and 326 candidate targets for treating COPD. Quercetin, Nobiletin, Kaempferol, Luteolin, Ginsenoside Rh2 and Formononetin were probably the main active compounds of SQTS in COPD treatment as they affected the most COPD-related targets, and interleukin-1(IL-6), signal transducing activator of transcription 3(STAT3), matrix metalloproteinase-9(MMP9), vascular endothelial growth factor A(VEGFA), protein kinase B(AKT1), hypoxiainducible factor-1α(HIF-1α), and forkhead box O3(FoxO3) were identified as the hub genes associated with its therapeutic effect. KEGG analysis was mainly enriched in the signaling pathways closely related to inflammation, immunity and oxidative stress, such as HIF-1, tumor necrosis factor(TNF), phosphatidylinositol 3 kinase(PI3K)-AKT, FoxO, apoptosis, IL-17, and toll-like receptor. Molecular docking confirmed that the main active components shared a good affinity with the hub genes. In vivo experiments, the SQTS was found to improve the body weight, exhaustive swimming time, tailhanging imm

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Effects of plants-associated microbiota on cultivation and quality of Chinese herbal medicines

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Chinese Herbal Medicines 2024年第2期16卷 190-203页

作者： Yue Ouyang Qiqing Cheng Chunsong Cheng Ziyu Tang Yufeng Huang Eyu Tan Shaofeng Ma xinheng Lin Ying Xie Hua Zhou Guangdong Provincial Hospital of Chinese Medicine Guangdong Provincial Academy of Chinese Medical SciencesState Key Laboratory of Dampness Syndrome of Chinese MedicineSecond Affiliated Hospital of Guangzhou University of Chinese MedicineGuangdong-Hong Kong-Macao Joint Lab on Chinese Medicine and Immune Disease ResearchGuangzhou 510006China Faculty of Chinese Medicine Macao University of Science and TechnologyMacao 999078China School of Pharmacy Hubei University of Science and TechnologyXianning 437100China Key Laboratory of Plant Ex-situ Conservation and Research Center of Resource Plant Lushan Botanical GardenChinese Academy of SciencesJiujiang 332900China Jiangmen Wuyi Hospital of Traditional Chinese Medicine Jiangmen 529020China Joint Laboratory for Translational Cancer Research of Chinese Medicine Ministry of EducationGuangzhou University of Chinese MedicineGuangzhou 510006China

Microbial resource influences the life activities of medicinal plants from several perspectives.Endophytes,rhizosphere microorganisms,and other environmental microorganisms play essential roles in medicinal plant growth and development,plant yield,and clinical efficacy.The microbiota can influence the biosynthesis of active compounds in medicinal plants by stimulating specific metabolic pathways.They induce host plants to improve their resistance to environmental stresses by accumulating secondary metabolites.Microorganisms can interact with their host plants to produce long-term,targeted selection results and improve their ability to adapt to the environment.Due to the interdependence and interaction between microorganisms and medicinal plants,Chinese herbal medicines(CHMs)quality is closely related to the associated microorganisms.This review summarizes the relationship between medicinal plants and their associated microorganisms,including their species,distribution,life activities,and metabolites.Microorganisms can aid in quality control,improve the efficacy of medicinal plants,and provide markers for identifying the origin and storage time of CHMs.Therefore,a comprehensive understanding of the relationship between microorganisms and medicinal plants will help to control the quality of CHMs from different perspectives.

关键词： Chinese herbal medicines endophytes environmental microorganisms microbiota rhizosphere microorganisms

Resource pre-allocation algorithms for low-energy task scheduling of cloud computing

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Journal of Systems Engineering and Electronics 2016年第2期27卷 457-469页

作者： Xiaolong Xu Lingling Cao xinheng wang College of Computer Nanjing University of Posts and Telecommunications Nanjing 210003 China School-of Computing University of the West of Scotland Paisley PA12BE UK

In order to lower the power consumption and improve the coefficient of resource utilization of current cloud computing systems, this paper proposes two resource pre-allocation algorithms based on the ＂shut down the redundant, turn on the demanded＂ strategy here. Firstly, a green cloud computing model is presented, abstracting the task scheduling problem to the virtual machine deployment issue with the virtualization technology. Secondly, the future workloads of system need to be predicted： a cubic exponential smoothing algorithm based on the conservative control（CESCC） strategy is proposed, combining with the current state and resource distribution of system, in order to calculate the demand of resources for the next period of task requests. Then, a multi-objective constrained optimization model of power consumption and a low-energy resource allocation algorithm based on probabilistic matching（RA-PM） are proposed. In order to reduce the power consumption further, the resource allocation algorithm based on the improved simulated annealing（RA-ISA） is designed with the improved simulated annealing algorithm. Experimental results show that the prediction and conservative control strategy make resource pre-allocation catch up with demands, and improve the efficiency of real-time response and the stability of the system. Both RA-PM and RA-ISA can activate fewer hosts, achieve better load balance among the set of high applicable hosts, maximize the utilization of resources, and greatly reduce the power consumption of cloud computing systems.

关键词： green cloud computing power consumption prediction resource allocation probabilistic matching simulated annealing

Multi-authority proxy re-encryption based on CPABE for cloud storage systems

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Journal of Systems Engineering and Electronics 2016年第1期27卷 211-223页

作者： Xiaolong Xu Jinglan Zhou xinheng wang Yun Zhang College of Computer Nanjing University of Posts and Telecommunications School of Computing University of the West of Scotland

The dissociation between data management and data ownership makes it difficult to protect data security and privacy in cloud storage systems.Traditional encryption technologies are not suitable for data protection in cloud storage systems.A novel multi-authority proxy re-encryption mechanism based on ciphertext-policy attribute-based encryption（MPRE-CPABE） is proposed for cloud storage systems.MPRE-CPABE requires data owner to split each file into two blocks,one big block and one small block.The small block is used to encrypt the big one as the private key,and then the encrypted big block will be uploaded to the cloud storage system.Even if the uploaded big block of file is stolen,illegal users cannot get the complete information of the file easily.Ciphertext-policy attribute-based encryption（CPABE）is always criticized for its heavy overload and insecure issues when distributing keys or revoking user＇s access right.MPRE-CPABE applies CPABE to the multi-authority cloud storage system,and solves the above issues.The weighted access structure（WAS） is proposed to support a variety of fine-grained threshold access control policy in multi-authority environments,and reduce the computational cost of key distribution.Meanwhile,MPRE-CPABE uses proxy re-encryption to reduce the computational cost of access revocation.Experiments are implemented on platforms of Ubuntu and CloudSim.Experimental results show that MPRE-CPABE can greatly reduce the computational cost of the generation of key components and the revocation of user＇s access right.MPRE-CPABE is also proved secure under the security model of decisional bilinear Diffie-Hellman（DBDH）.

关键词： cloud storage data partition multi-authority security proxy re-encryption attribute-based encryption（ABE）.

Variance-based fingerprint distance adjustment algorithm for indoor localization

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Journal of Systems Engineering and Electronics 2015年第6期26卷 1191-1201页

作者： Xiaolong Xu Yu Tang xinheng wang Yun Zhang College of Computer Nanjing University of Posts and Telecommunications School of Computing University of the West of Scotland

The multipath effect and movements of people in indoor environments lead to inaccurate localization. Through the test, calculation and analysis on the received signal strength indication （RSSI） and the variance of RSSI, we propose a novel variance-based fingerprint distance adjustment algorithm （VFDA）. Based on the rule that variance decreases with the increase of RSSI mean, VFDA calculates RSSI variance with the mean value of received RSSIs. Then, we can get the correction weight. VFDA adjusts the fingerprint distances with the correction weight based on the variance of RSSI, which is used to correct the fingerprint distance. Besides, a threshold value is applied to VFDA to improve its performance further. VFDA and VFDA with the threshold value are applied in two kinds of real typical indoor environments deployed with several Wi-Fi access points. One is a quadrate lab room, and the other is a long and narrow corridor of a building. Experimental results and performance analysis show that in indoor environments, both VFDA and VFDA with the threshold have better positioning accuracy and environmental adaptability than the current typical positioning methods based on the k-nearest neighbor algorithm and the weighted k-nearest neighbor algorithm with similar computational costs.

关键词： indoor localization fingerprint localization receivedsignal strength indication （RSSI） variance fingerprint distance.

Internet Multimedia Traffic Classification from QoS Perspective Using Semi-Supervised Dictionary Learning Models

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China Communications 2017年第10期14卷 202-218页

作者： Zaijian wang Yuning Dong Shiwen Mao xinheng wang College of Physics and Electronic Information Anhui Normal University Wuhu 241000 China College of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing 210003 China Department of Electrical and Computer Engineering Auburn University Auburn AL 36849-5201 USA School of Computing University of the West of Scotland Paisley PA 1 2BE UK

To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modified K-Singular Value Decomposition(K-SVD) method for multimedia identification. After analyzing several instances of typical Internet multimedia traffic captured in a campus network, this paper defines a new set of QoS classes according to the difference in downstream/upstream rates and proposes a modified K-SVD method that can automatically search for underlying structural patterns in the QoS characteristic space. We define bagQoS-words as the set of specific QoS local patterns, which can be expressed by core QoS characteristics. After the dictionary is constructed with an excess quantity of bag-QoSwords, Locality Constrained Feature Coding(LCFC) features of QoS classes are extracted. By associating a set of characteristics with a percentage of error, an objective function is formulated. In accordance with the modified K-SVD, Internet multimedia traffic can be classified into a corresponding QoS class with a linear Support Vector Machines(SVM) clas-sifier. Our experimental results demonstrate the feasibility of the proposed classification method.

关键词：多媒体业务 Internet 分类器业务流量学习模型字典监督特征编码

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近视眼像差与人眼客观焦深的关联性分析

中华实验眼科杂志 2024年第4期42卷 347-353页

作者：张明栋王雁赵新恒天津医科大学眼科临床学院、天津市眼科医院、天津市眼科研究所、天津市眼科学与视觉科学重点实验室、南开大学附属眼科医院、南开大学眼科学研究院天津300020

目的探讨低、高阶像差与人眼客观焦深的关联性及影响客观焦深的因素。方法采用横断面研究,选取2022年2—4月在天津市眼科医院屈光手术中心就诊的近视及散光患者76例152眼,其中男41例,女35例。采用iTrace视功能分析仪手动选择3、4、5和6 ... 详细信息

目的探讨低、高阶像差与人眼客观焦深的关联性及影响客观焦深的因素。方法采用横断面研究,选取2022年2—4月在天津市眼科医院屈光手术中心就诊的近视及散光患者76例152眼,其中男41例,女35例。采用iTrace视功能分析仪手动选择3、4、5和6 mm 4个扫描直径,对患者的全眼低阶像差和高阶像差进行测量,同时直接测得患者在该瞳孔直径下的客观焦深。采用Spearman秩相关性分析评估不同瞳孔直径下客观焦深与低、高阶像差之间的相关性;对不同程度近视和散光人群及不同瞳孔直径、眼别、性别间的客观焦深进行比较。结果客观焦深与总低阶像差在3、4、5、6 mm瞳孔直径下均呈正相关(r_(s)=0.380、0.317、0.385、0.519,均P0.05)。结论人眼客观焦深大小主要与高阶像差中垂直彗差和三叶草像差以及低阶像差中离焦量具有较强关联性,水平彗差和球差仅在瞳孔直径较大时与客观焦深相关性较强;此外,瞳孔直径较小(3 mm)或较大时(6 mm)明显影响客观焦深。

关键词：近视像差视觉质量客观焦深低阶像差高阶像差

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基于改进ConvNeXt模型的黄羽鸡表皮层黑色素智能分级方法

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农业工程学报 2024年第3期40卷 165-174页

作者：肖德琴陈芳玲刘又夫谢青梅张新珩华南农业大学农业农村部华南热带智慧农业技术重点实验室广州510642 华南农业大学数学与信息学院广州510642 华南农业大学动物科学学院广州510642

为解决活体黄羽鸡表皮层黑色素分级方法成本高、效率低下、分级环境易受环境光干扰等问题,该研究探索一种基于ConvNeXt模型的黄羽鸡表皮层黑色素智能分级方法ConvNeXt-WPCA,用于实现活体黄羽鸡表皮层黑色素智能分级。ConvNeXt-WPCA模型... 详细信息

为解决活体黄羽鸡表皮层黑色素分级方法成本高、效率低下、分级环境易受环境光干扰等问题,该研究探索一种基于ConvNeXt模型的黄羽鸡表皮层黑色素智能分级方法ConvNeXt-WPCA,用于实现活体黄羽鸡表皮层黑色素智能分级。ConvNeXt-WPCA模型通过以下3点改进提高模型对黄羽鸡黑色素的识别效果:1)针对黄羽鸡黑色素图像RGB三通道内黑色素信息分布不均衡问题,改变输入图片通道权重来增强模型对黑色素特征的提取能力;2)使用部分卷积代替深度可分离卷积,减少模型计算量和内存访问次数提高对计算资源的利用率;3)引入坐标注意力机制,引导模型关注黄羽鸡胸腹部及肛门附近皮肤提升模型精度。同时,该研究还设计一种双光源图像获取装置,分别在自然光和偏振光条件下拍摄黄羽鸡样本,以减小分级结果受环境光干扰的影响,并探索偏振光在黑色素分级任务中的应用潜力。结果表明ConvNeXt-WPCA模型相较标准ConvNeXt模型,针对自然光下黄羽鸡黑色素图像数据集分级准确率提升9.68个百分点,最终达到89.03%的识别准确率,针对偏振光下黄羽鸡黑色素图像数据集分级准确率提升15.26个百分点,最终达到98.87%的识别准确率。该研究证实基于偏振光条件获取的黄羽鸡表皮层黑色素图像分级效果优于自然光条件,提出的ConvNeXt-WPCA黄羽鸡表皮层黑色素分级方法识别准确率高,同时模型参数量及浮点计算量均有降低,为黄羽鸡表皮层黑色素智能分级实际应用提供了理论基础及技术支持。

关键词：模型深度学习注意力机制黄羽鸡 ConvNeXt 品质分级偏振光