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35th Irish Systems and Signals Conference, ISSC 2024

作者： Lu, Rong-Hui Chen, Tzong-Jer Pan, Junhong Wuyi University Information Technology and Laboratory Management Center Fujian Wuyishan354300 China School of Mathematics and Computer Science Wuyi University Fujian Wuyishan354300 China Wuyi University Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry China

ISBN: (纸本)9798350352986

Image sharpening prediction plays a significant role in image restoration, forensics, and computer vision. This work proposes a new method for detecting sharpening and indexing in digital images. Images initially undergo manipulation using Gaussian filters and Unsharp Masking (UM) with various degrees of intensity. This is followed by transformation into the frequency domain using Fourier transform. The resulting amplitude values are calculated, and their absolute values are summed along the frequency axis to produce a profile curve. These data curves are subsequently smoothed. The UM curves exhibit concavity and symmetry relative to the curve of the original image. The values of these curves at the concave positions, corresponding to various UM intensities, can be utilized as a sharpness index by comparing them to the curve values of the original image at the same positions. These sharpness indices are dependent on the UM amplification factors. This methodology finds utility in medical image processing and computer vision systems for sharpness detection and indexing. The features identified in this study, based on Fourier power spectrum analysis, can be input into a machine learning classifier for classification. © 2024 IEEE.

关键词： Fourier transforms

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A Local Regression-Based SLA-Aware Energy-Efficient Virtual Machine Consolidation Algorithm in data Centers 9

A Local Regression-Based SLA-Aware Energy-Efficient Virtual ...

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9th International Conference on big data Computing and Communications, bigCom 2023

作者： Wu, Xiaodong Wang, Ronghai Lin, Guoxin Quanzhou Normal University School of Mathmatics and Computer Science China Fujian Province University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian China Provincial Big Data Research Institute of Intelligent Manufacturing Quanzhou China

ISBN: (纸本)9798350331240

Virtual Machine Consolidation (VMC) is an effective means to improve the resource utilization and reduce energy consumption of data centers. However, the VMC optimization that only considers energy consumption may lead to SLA (Service-Level Agreements) violations and thus fail to guarantee the QoS (Quality of Service) provided. Therefore, it is necessary to consider both energy saving and SLA violations in the process of VMC. In this paper, we propose an energy and SLA-aware VMC algorithm that considers both energy saving and SLA violation optimization in data centers. The proposed algorithm uses the local regression method to predict the utilization of virtual machines, based on which it determines whether the host will be overloaded, so as to make the decision of virtual machine migrations. The proposed VMC algorithm is evaluated with CloudSim toolkit. Simulation results demonstrate that our algorithm has good energy saving performance and can effectively reduce SLA violation in cloud data center. Considering energy consumption and SLA violation, the ESV can be reduced by 7.57% on average and 16.36% at most compared with other algorithms. © 2023 IEEE.

关键词： Virtual machine

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Contrastive Learning for Robust Android Malware Familial Classification

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IEEE Transactions on Dependable and Secure Computing 2022年 1-14页

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China University of Texas at Dallas Dallas USA National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. IEEE

关键词： Semantics

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ANIMC: A Soft Approach for Autoweighted Noisy and Incomplete Multiview Clustering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 192-206页

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering The School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Multiview clustering has wide real-world applications because it can process data from multiple sources. However, these data often contain missing instances and noises, which are ignored by most multiview clustering methods. Missing instances may make these methods difficult to use directly, and noises will lead to unreliable clustering results. In this article, we propose a novel autoweighted noisy and incomplete multiview clustering (ANIMC) approach via a soft autoweighted strategy and a doubly soft regular regression model. First, by designing adaptive semiregularized nonnegative matrix factorization, the soft autoweighted strategy assigns a proper weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Second, by proposing θ-norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing different θ. Compared with previous methods, ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our approach in different scenarios, thereby improving its generalization ability;2) it automatically learns a proper weight for each view, thereby reducing the influence of noises;3) it performs doubly soft regularized regression that aligns the same instances in different views, thereby decreasing the impact of missing instances. Extensive experimental results show its superior advantages over other state-of-the-art works. © 2020 IEEE.

关键词： Clustering algorithms

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Multivariate Prediction of Retail Sales by Multi-task Time Series Learning

Multivariate Prediction of Retail Sales by Multi-task Time S...

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2021 International Conference on computer Graphics, Artificial Intelligence, and data Processing, ICCAID 2021

作者： Lin, Miaopei Yu, Jianxing Yin, Jian Sun Yat-sen University School of Computer Science and Engineering Guangdong Key Laboratory of Big Data Analysis and Processing China

ISBN: (数字)9781510652170

ISBN: (纸本)9781510652163

Predicting retail sales is a hot research topic that can help firms achieve on-demand procurement. That can reduce the extra costs caused by an inventory shortage or surpluses. Traditional methods usually regard the sales of a certain product as the independent time series and then solve the task by a curve fitting model. However, the sales volume is not a single value, but the one aggregated from many types of products. There are correlations between different types of product sales. The increase in one type of product sales may lead to changes in sales in another type. Without capturing the correlation between different product sales, it is difficult to obtain satisfactory performance. To solve this problem, we propose a new multi-task method based on multivariate time series learning. Considering there are multiple product types and each type has different trend characteristics, we first cluster the time series of all product types and then model each as a multivariate prediction task. We then design a new dilated convolution network to fusion the features of related products and the trend characteristic of each task. Moreover, we develop a trend structural entropy network to grasp the fluctuation features of the task. A new self-enhancement mechanism is proposed to finely capture the correlations among tasks. Through multi-task learning, the model can effectively improve prediction accuracy by using the complementary information of closely related time series. Experimental results on two real data sets show the effectiveness of our approach. © 2022 SPIE.

关键词： Sales

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Pulsed polarized vortex beam enabled by metafiber lasers

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PhotoniX 2024年第1期5卷 110-120页

作者： Chenxi Zhang Lei Zhang He Zhang Bo Fu Jiyong Wang Min Qiu Key Laboratory of Precision Opto-Mechatronics Technology of Education Ministry School of Instrumentation and Optoelectronic EngineeringBeihang UniversityBeijing 100191China Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province School of EngineeringWestlake University18 Shilongshan RoadHangzhou 310024Zhejiang ProvinceChina Institute of Advanced Technology Westlake Institute for Advanced Study18 Shilongshan RoadHangzhou 310024Zhejiang ProvinceChina QianYuan National Laboratory Hangzhou 310000China Key Laboratory of Big Data-Based Precision Medicine Ministry of Industry and Information Technology School of Engineering MedicineBeihang UniversityBeijing 100191China Ministry of Education Engineering Research Center of Smart Microsensors and Microsystems School of Electronics and InformationHangzhou Dianzi UniversityHangzhou 310018China Westlake Institute for Optoelectronics FuyangHangzhou 311421China

Pulsed polarized vortex beams,a special form of structured light,are generated by tailoring the light beam spatiotemporally and witness the growing application demands in nonlinear optics such as ultrafast laser processing and surface plasma ***,existing techniques for generating polarized vortex beams suffer from either low compactness due to the use of bulky components or limited controlment of pulse ***,an all-fiber technique combining plasmonic metafibers with mode conversion method is harnessed to generate high-performance pulsed polarized vortex *** metafibers are utilized as saturable absorbers to produce Q-switched pulses with micro-second duration,while the offset splicing method is employed to partially convert the fundamental transverse mode(LP_(01))to higher-order mode(LP_(11)).Eventually,a polarized vortex beams laser is achieved at the telecom band with a repetition frequency of 116.0 *** impact of geometrical parameters including period of metafibers and offset of splicing on the spatiotemporal properties of pulsed polarized vortex beams is systematically *** findings could pave the way for design,control and generation of all-fiber pulsed polarized vortex beams,and also offer insights into the development of other types of structured laser sources.

关键词： Metafiber Q-switched pulse Polarized vortex beam

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An intelligent solar flare prediction model based on X-ray flux curves using Long Short-Term Memory

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Astronomical Techniques and Instruments 2025年第2期2卷 65-72页

作者： Yan Gao Li Zhang Long Xu College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang 550025China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China

Solar flares are violent solar outbursts which have a great influence on the space environment surrounding Earth,potentially causing disruption of the ionosphere and interference with the geomagnetic field,thus causing magnetic ***,it is very important to accurately predict the time period of solar *** paper proposes a flare prediction model,based on physical images of active solar *** employ X-ray flux curves recorded directly by the Geostationary Operational Environmental Satellite,used as input data for the model,allowing us to largely avoid the influence of accidental errors,effectively improving the model prediction efficiency.A model based on the X-ray flux curve can predict whether there will be a flare event within 24 *** reverse can also be verified by the peak of the X-ray flux curve to see if a flare has occurred within the past 24 *** True Positive Rate and False Positive Rate of the prediction model,based on physical images of active regions are 0.6070 and 0.2410 respectively,and the accuracy and True Skill Statistics are 0.7590 and *** model can effectively improve prediction efficiency compared with models based on the physical parameters of active regions or magnetic field records,providing a simple method for solar flare prediction.

关键词： Neural Network Long Short-Term Memory Solar flare prediction X-ray flux curve

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A topic detection method based on KM-LSH Fusion algorithm and improved BTM model

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Soft Computing 2024年第19期28卷 11421-11438页

作者： Liu, Wenjun Guo, Huan Gan, Jiaxin Wang, Hai Wang, Hailan Zhang, Chao Peng, Qingcheng Sun, Yuyan Yu, Bao Hou, Mengshu Li, Bo Li, Xiaolei School of Computer and Software Engineering XiHua University Chengdu610039 China Chengdu611731 China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou646000 China School of Big Data and Artificial Intelligence Chengdu Technological University Chengdu611730 China Sichuan Provincial Engineering Research Center of Hydroelectric Energy Power Equipment Technology Chengdu610039 China

Topic detection is an information processing technology designed to help people deal with the growing problem of data information on the Internet. In the research literature, topic detection methods are used for topic classification through word embedding, supervised-based and unsupervised-based approaches. However, most methods for topic detection only address the problem of clustering and do not focus on the problem of topic detection accuracy reduction due to the cohesiveness of topics. Also, the sequence of biterm during topic detection can cause substantial deviations in the detected topic content. To solve the above problems, this paper proposes a topic detection method based on KM-LSH fusion algorithm and improved BTM model. KM-LSH fusion algorithm is a fusion algorithm that combines K-means clustering and LSH refinement clustering. The proposed method can solve the problem of cohesiveness of topic detection, and the improved BTM model can solve the influence of the sequence of biterm on topic detection. First, the text vector is constructed by processing the collected set of microblog texts using text preprocessing methods. Secondly, the KM-LSH fusion algorithm is used to calculate text similarity and perform topic clustering and refinement. Finally, the improved BTM model is used to model the texts, which is combined with the word position and the improved TF-IDF weight calculation algorithm to adjust the microblogging texts in clustering. The experiment results indicate that the proposed KM-LSH-IBTM method improves the evaluation indexes compared with the other three topic detection methods. In conclusion, the proposed KM-LSH-IBTM method promotes the processing capability of topic detection in terms of cohesiveness and the sequence of biterm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： K-means clustering

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Research on water extraction method from remote sensing images of lakes in cold and arid regions based on deep learning 3

Research on water extraction method from remote sensing imag...

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3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Wang, Xin Fu, Xueliang Hu, Hua Li, Honghui College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research Application of Agriculture and Animal Husbandry Hohhot China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

Semantic segmentation technology is an important research direction in the field of computer vision, with the continuous improvement of computer GPU and other hardware arithmetic power, as well as the more clear and accurate remote sensing images. In recent years, deep learning technology has developed rapidly in the field of semantic segmentation, and the level of semantic segmentation technology has improved significantly. Based on unsupervised, automated, end-to-end deep learning networks have gradually become the object of research in scene object segmentation, pre-person background segmentation, face recognition and other related fields. In this paper, we conduct research on deep learning semantic segmentation algorithms for water body extraction, focusing on specific analysis of typical problems such as long freezing period of lakes in cold and arid regions, low precipitation, complex seasonal changes of lakes, and fragile water bodies. The cold and arid zone lakes distributed in Inner Mongolia region of China are selected as the research objects, and the water body extraction experiments are conducted on the remote sensing images of Hulun Lake and Wuoliangsuhai Lake. Propose a SER34AUnet model, the model was improved based on the Unet network model of Encode-Decode architecture, and finally achieved good results in the water extraction experiments. The main work of this paper is as follows: (1) Establishing image segmentation dataset: remote sensing images of several lakes in cold and arid regions in different seasons, different satellites and various remote sensing images with specific representative features typical of cold and arid regions are used to enhance the generalization ability of the dataset. (2) Improving the Unet model: Firstly, the Resnet34 network, which is more capable of extracting image features, is used in the Encode part. Secondly, the SE (Queeze and-Excitation) module based on the channel attention mechanism is added to the BasicBlock mo

关键词： Lakes

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A novel single-phase Ca3Gd2Si3O12:Dy3+, Sm3+ phosphor exhibiting high thermal stability for solid-state lighting applications

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Ceramics International 2025年

作者： Zhang, Yanzhi Deng, Xiaoxu Cui, Ruirui Guo, Xiang Zhang, Jun Deng, Chaoyong Key Laboratory of Functional Composite Materials of Guizhou Province College of Big Data and Information Engineering Guizhou University Guiyan550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China College of Computer and Information Engineering Guizhou University of Commerce Guiyang550014 China

In order to obtain efficient single-phase co-doped phosphors with excellent luminescence properties under near-ultraviolet excitation, Ca3Gd2Si3O12:xDy3+ (CGSO: xDy3+) and Ca3Gd2Si3O12:0.08Dy3+, ySm3+ (CGSO:0.08Dy3+, ySm3+) phosphors were effectively manufactured using the classical high-temperature solid-phase method. The phase structure, morphology and XPS test results of the phosphor all show that the Ca3Gd2Si3O12(CGSO) material has been successfully synthesized, and Dy3+ and Sm3+ ions have been successfully doped into the matrix lattice. Under 362 nm illumination, the PL spectra of CGSO:0.08Dy3+, ySm3+ phosphor revealed that the typical peak intensity of Dy3+ ions declined as the amount of Sm3+ ions increased, whereas the distinctive peak intensity of Sm3+ ions achieved its maximum at y = 0.07. As Sm3+ doping grows larger, the fluorescence lifetime shortens, signifying the occurrence of transferring energy among Dy3+ and Sm3+ ions, with a maximum efficiency of 81 % being attained. The Ca3Gd2Si3O12:0.08Dy3+, ySm3+ phosphor demonstrates a commendable thermal stability of 90.17 %, with adjustments to the Sm3+ concentration revealing the potential to yield warm white light, as evidenced by both CIE chromaticity coordinates and CCT measurements. In addition, compared with the phosphor containing only Dy3+, the LED assembled with CGSO:0.08Dy3+, ySm3+ phosphor containing Sm3+ ions and 365 nm LED chip emits warmer white light with lower color temperature, which confirms the application value of CGSO:0.08Dy3+, ySm3+ phosphor in the domain of solid-state lighting. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Semiconductor doping

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